EXAMINING THE IMPACT OF CLIMATE CHANGE ON
DENGUE TRANSMISSION IN THE ASIA-PACIFIC
REGION
Shahera Banu Doctor of Veterinary Medicine, Master of Science
A thesis submitted for the degree of Doctor of Philosophy
School of Public Health and Social Work
Faculty of Health
Queensland University of Technology
November 2013
Examining the impact of climate change on dengue transmission in the Asia-Pacific Region i
Keywords
Dengue fever
Climate change
Scan statistics
Space-time cluster
Distributed Lag Model
Socio-environmental factors
Geographic Information System
ii Examining the impact of climate change on dengue transmission in the Asia-Pacific Region
Abstract
Dengue fever (DF) is a serious public health concern in many parts of the
world. An increase in DF incidence has been observed globally over the past
decades. Multiple factors including urbanisation, increased international travels and
global climate change are thought to be responsible for increased DF. However, little
research has been conducted in the Asia-Pacific region about the impact of these
changes on dengue transmission. The overarching aim of this thesis is to explore the
spatiotemporal pattern of DF transmission in the Asia-Pacific region and project the
future risk of DF attributable to climate change.
Annual data of DF outbreaks for sixteen countries in the Asia-Pacific region
over the last fifty years were used in this study. The results show that the geographic
range of DF in this region increased significantly over the study period. Thailand,
Vietnam and Laos were identified as the highest risk areas and there was a southward
expansion observed in the transmission pattern of DF which might have originated
from Philippines or Thailand. Additionally, the detailed DF data were obtained and
the space-time clustering of DF transmission was examined in Bangladesh. Monthly
DF data were used for the entire country at the district level during 2000-2009.
Dhaka district was identified as the most likely DF cluster in Bangladesh and several
districts of the southern part of Bangladesh were identified as secondary clusters in
the years 2000-2002.
In order to examine the association between meteorological factors and DF
transmission and to project the future risk of DF using different climate change
scenarios, the climate-DF relationship was examined in Dhaka, Bangladesh. The
results show that climate variability (particularly maximum temperature and relative
humidity) was positively associated with DF transmission in Dhaka. The effects of
climate variability were observed at a lag of four months which might help to
potentially control and prevent DF outbreaks through effective vector management
and community education. Based on the quantitative assessment of the climate-DF
Examining the impact of climate change on dengue transmission in the Asia-Pacific Region iii
relationship, projected climate change will likely increase mosquito abundance and
activity and DF in this area. Assuming a temperature increase of 3.3oC without any
adaptation measures and significant changes in socio-economic conditions, the
consequence will be devastating, with a projected annual increase of 16,030 cases in
Dhaka, Bangladesh by the end of this century. Therefore, public health authorities
need to be prepared for likely increase of DF transmission in this region.
This study adds to the literature on the recent trends of DF and impacts of
climate change on DF transmission. These findings may have significant public
health implications for the control and prevention of DF, particularly in the Asia-
Pacific region.
iv Examining the impact of climate change on dengue transmission in the Asia-Pacific Region
Table of Contents
Keywords .................................................................................................................................................. i
Abstract ................................................................................................................................................... ii
Table of Contents ................................................................................................................................... iv
List of Figures ....................................................................................................................................... viii
List of Tables ........................................................................................................................................... ix
List of Abbreviations ................................................................................................................................ x
Statement of Original Authorship .......................................................................................................... xi
Acknowledgements ............................................................................................................................... xii
CHAPTER 1: INTRODUCTION........................................................................................................ 1
1.1 Background .................................................................................................................................. 1
1.2 Research Aims .............................................................................................................................. 4
1.3 Thesis Structure ........................................................................................................................... 4
CHAPTER 2: LITERATURE REVIEW ................................................................................................ 7
2.1 Characteristics and Ecology of DF ................................................................................................ 7
2.2 Determinants of DF transmission ................................................................................................ 9
2.3 Emergence and geographic spread of DF .................................................................................. 11
2.4 Climate variability and DF .......................................................................................................... 13
2.5 Global climate change and DF ................................................................................................... 15
Systematic review ................................................................................................................................. 16
Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors 16
2.6 Summary .................................................................................................................................... 17
2.7 Introduction ............................................................................................................................... 18
2.8 Methods ..................................................................................................................................... 20
2.8.1 Inclusion criteria ............................................................................................................. 22
2.9 Results ........................................................................................................................................ 24
2.9.1 Association between climatic factors and DF ................................................................. 24
Examining the impact of climate change on dengue transmission in the Asia-Pacific Region v
2.9.2 Association between socio-environmental factors and DF ............................................ 32
2.10 Conclusion .................................................................................................................................. 35
2.11 Update of the systematic review ............................................................................................... 42
2.11.1 Association between climatic factors and DF ................................................................. 42
2.11.2 Association between socio-environmental factors and DF ............................................ 43
2.12 Knowlege Gaps........................................................................................................................... 44
CHAPTER 3: RESULTS PAPER 1 ................................................................................................... 45
Dynamic spatiotemporal trends of dengue transmission in the Asia-Pacific region during 1955–2004 45
3.1 Abstract ...................................................................................................................................... 46
3.2 Introduction ............................................................................................................................... 47
3.3 Methods ..................................................................................................................................... 48
3.3.1 Study area ....................................................................................................................... 48
3.3.2 Data collection ................................................................................................................ 49
3.3.3 Data Analyses.................................................................................................................. 50
3.4 Results ........................................................................................................................................ 56
3.4.1 Descriptive statistics ....................................................................................................... 56
3.4.2 Trends of DF transmission .............................................................................................. 56
3.4.3 Space-time clusters ......................................................................................................... 59
3.5 Discussion .................................................................................................................................. 59
CHAPTER 4: RESULTS PAPER 2 ................................................................................................... 67
Space-time clusters of dengue fever in Bangladesh ............................................................................. 67
4.1 Summary .................................................................................................................................... 68
4.2 Introduction ............................................................................................................................... 69
4.3 Methods ..................................................................................................................................... 70
4.3.1 Study area ....................................................................................................................... 70
4.3.2 Data collection ................................................................................................................ 70
4.3.3 Statistical analysis ........................................................................................................... 71
4.4 Results ........................................................................................................................................ 72
4.4.1 DF epidemics and outbreaks .......................................................................................... 72
vi Examining the impact of climate change on dengue transmission in the Asia-Pacific Region
4.4.2 Disease clusters .............................................................................................................. 72
4.4.3 Spatial dispersion of DF .................................................................................................. 77
4.5 Discussion .................................................................................................................................. 78
4.6 Conclusion .................................................................................................................................. 80
CHAPTER 5: RESULTS PAPER 3 ................................................................................................... 85
Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh. ................... 85
5.1 Abstract ...................................................................................................................................... 86
5.2 Introduction ............................................................................................................................... 87
5.3 Materials and methods .............................................................................................................. 89
5.3.1 Study area ....................................................................................................................... 89
5.3.2 Data collection ................................................................................................................ 89
5.3.3 Data analysis ................................................................................................................... 90
5.4 Results ........................................................................................................................................ 93
5.5 Discussion ................................................................................................................................ 101
5.6 Conclusion ................................................................................................................................ 105
CHAPTER 6: GENERAL DISCUSSION .......................................................................................... 110
6.1 Substantive discussion ............................................................................................................. 110
6.2 Implications of the research findings ....................................................................................... 115
6.3 Strengths of this thesis ............................................................................................................. 116
6.4 Limitations of this thesis .......................................................................................................... 117
6.5 Future research directions ....................................................................................................... 118
6.5.1 Better understand the ecology of DF ........................................................................... 118
6.5.2 Advance the risk assessment techniques ..................................................................... 118
6.5.3 Improve disease surveillance and monitoring .............................................................. 119
6.5.4 Evaluate the effectiveness of the public health interventions ..................................... 119
6.5.5 Translate research into policy and risk management practice ..................................... 119
6.6 Conclusions .............................................................................................................................. 120
BIBLIOGRAPHY 121
APPENDICES 137
Examining the impact of climate change on dengue transmission in the Asia-Pacific Region vii
Appendix A 137
Appendix B 138
Appendix C 139
viii Examining the impact of climate change on dengue transmission in the Asia-Pacific Region
List of Figures
Figure 2.1 Transmission cycle of dengue fever ....................................................................................... 8
Figure 2.2 Determinants of dengue transmission (Sutherst, 2004) ...................................................... 10
Figure 2.3 Countries at risk of DF transmission, 2008 (Source: World Health Organization, http://www.nathnac.org/pro/factsheets/images/clip_image002_015.jpg)........................ 12
Figure 2.4 Flow diagram of article selection process ............................................................................ 23
Figure 3.1 Total numbers of countries with DF outbreaks in the Asia-Pacific region, during 1955-2004. (Data source: WHO DengueNet) ....................................................................... 53
Figure 3.2 Cumulative incidence of DF in the Asia-Pacific countries. A: 1955-1964, B: 1965-1974, C: 1975-19854,D:1985-1994,E:1995-2004.The X and Y axis of the map show the longitude and latitude respectively. .............................................................................. 55
Figure 3.3 Space-time clusters of DF transmission in the Asia-Pacific region. A: 1955-1964, B: 1965-1974,C:1975-19854,D:1985-1994,E:1995-2004. The X and Y axis of the map show the longitude and latitude respectively. ..................................................................... 58
Figure 4.1 Monthly number of DF cases, deaths and districts with DF notification between January 2000 and December 2009 in Bangladesh. .............................................................. 73
Figure 4.2. Space-time clusters of DF identified in three different periods in Bangladesh. .................. 74
Figure 5.1 Auto-correlation function partial auto-correlation function and scatter plot of residuals for DLM. ................................................................................................................ 97
Figure 5.2 Association between climatic variables (maximum temperature and relative humidity) and DF at different lags. ....................................................................................... 98
Figure 5.3 Validated distributed lag model of climate variation in Dhaka, Bangladesh (validation period = Jan 2009 – Dec 2010 i.e., the cross validation period). ........................ 99
Figure 6.1 Framework of research findings in this thesis .................................................................... 112
Examining the impact of climate change on dengue transmission in the Asia-Pacific Region ix
List of Tables
Table 2.1 Search strategy ...................................................................................................................... 21
Table 2.2 Characteristics of studies on the association between climatic variables and DF transmission ......................................................................................................................... 25
Table 2.3 Characteristics of studies on the association between Socio-environmental factors and DF transmission ............................................................................................................. 34
Table 3.1 Summary statistics for annual number of DF cases in the Asia-Pacific countries during 1955-2004 ................................................................................................................. 52
Table 3.2 Space-time clusters of DF transmission in the Asia-Pacific region during 1955- 2004 .......... 57
Table 4.1 Space-time clusters of DF in Bangladesh, 2000-2009. ........................................................... 75
Table 5.1 Descriptive statistics of monthly climatic conditions and DF in Dhaka, Bangladesh, 2000-2010. ........................................................................................................................... 94
Table 5.2 Spearman’s correlation coefficients between monthly climatic variations in Dhaka, Bangladesh. .......................................................................................................................... 94
Table 5.3 Deviances for the relationship between DF and different temperature measures by DLM ...................................................................................................................................... 95
Table 5.4 Deviances for DLM using different covariates ....................................................................... 95
Table 5.5: Sensitivity and specificity of DLM for dengue occurrence. .................................................. 96
Table 5.6 Changes in annual DF incidence under different scenarios of temperature increase by 2100 in Dhaka, Bangladesh. .......................................................................................... 100
x Examining the impact of climate change on dengue transmission in the Asia-Pacific Region
List of Abbreviations
AIC Akaike Information Criterion
ARIMA Autoregressive Integrated Moving Average
BBS Bangladesh Bureau of Statistics
CIA Central Intelligence Agency
DF Dengue Fever
DHF Dengue Hemorrhagic Fever
DSS Dengue Shock Syndrome
DGHS Directorate General of Health Services
DLM Distributed Lag Model
ENSO EI Niño-Southern Oscillation
GIS Geographic Information System
IPCC Intergovernmental Panel on Climate Change
RR Relative Risk
SARIMA Seasonal Autoregressive Integrated Moving Average
SOI Southern Oscillation Index
WHO World Health Organization
QUT Verified Signature
xii Examining the impact of climate change on dengue transmission in the Asia-Pacific Region
Acknowledgements
I am grateful to my Principal Supervisor Professor Shilu Tong who provided
me with an opportunity to do a postgraduate study at QUT. I want to thank him for
his constant support, generosity, patience and unlimited time for help. It has been a
long journey but very pleasant. I would also like to thank my associate supervisors
Dr. Wenbiao Hu and Dr. Cameron Hurst for their kindness and helpful advice on
study design and research findings. Thanks to Dr. Yuming Guo for his assistance on
GIS techniques and R software.
I would also like to thank Dr. Su Naish, Dr. Wei Wei Yu, Dr. Lyle Turner, Mr.
Cunrui Huang, Ms. Xiaofang Ye, Ms. Xiaoyu Wang, Ms. Yan Bi, Mr. Xin Qi, Mr.
Zhiwei Xu, Ms. Jia Jia Wang and other colleagues for their help with my research.
Thanks to the Directorate General of Health Services, Bangladesh and
Bangladesh Meteorological Department for providing valuable data on dengue and
climate, respectively.
Thanks to the Faculty of Health for providing me with the Post Graduate
research Scholarship. I want to thank all staff members in the School of Public
Health (SPH), Research Student Centre and IT help desk who have provided an
excellent and convenient environment for postgraduate research at QUT.
Finally, I am grateful to my family for their love, inspiration and support,
without which I would not have gone so far as I did.
Chapter 1: Introduction 1
Chapter 1: Introduction
1.1 BACKGROUND
Dengue fever (DF) is one of the most important emerging arboviral diseases
worldwide (WHO, 2012a). The disease is caused by one of the four viral serotypes of
the genus flavivirus. Dengue virus transmits through the bite of container breeding
mosquitoes Aedes aegypti and Aedes albopictus, which occur in most tropical and
subtropical countries (Simmons et al., 2012, Gibbons, 2010). Symptoms of DF vary
from febrile illness to severe haemorrhagic fever (Guzman et al., 2010, Gubler,
2004b). Up to 100 million infections occur annually and 22,000 children die due to
DF globally (Guzman et al., 2010, Beatty et al., 2011, Gibbons, 2010). More than
50% of the world’s population live in over 100 countries which are currently at risk
of DF. According to the World Health Organization (WHO), DF transmission
increased by 30-fold worldwide over the last 50 years and the transmission
predominantly increased in urban and semi-urban areas (WHO, 2012a). DF has
become an increasing public health concern around the world due to its serious
health consequences including death and the lack of effective vaccine and specific
treatment.
The risk of DF transmission varies over space and time and the dynamics of
disease depend on individual, household and climatic factors (Mackenzie et al.,
2004). Weather variation, virus strains, mosquito densities, human activities and
movement and population immunity, all contribute to the transmission of DF
(Gubler, 2011). Transmission is higher in urban and semi-urban areas where
increased populations live in close contact with increased mosquito densities. DF
transmission is also higher in areas where two or more viral serotypes circulate
simultaneously (Rigau-Perez et al., 1998). International trade and increased air travel
potentially enhanced the dispersion of Aedes mosquitoes and the virus in recent
years.
2 Chapter 1: Introduction
DF typically follows a seasonal pattern which suggests a correlation between
weather and the pattern of DF (Reiter, 2001, Johansson et al., 2009a). Precipitation,
temperature and relative humidity are the most important weather factors attributed
to the growth and dispersion of mosquito vector and potential of DF outbreaks
(Chaturvedi et al., 2006, Chowell and Sanchez, 2006, Ram et al., 1998, Wu et al.,
2007). Temperature influences the life cycle of Aedes mosquitoes including
development rates and larval survival, timing of blood meal and the length of
reproductive cycle (Patz et al., 2005). Temperature can also affect the virus
replication, maturation and period of infectivity within the vector. Higher
temperature decreases the length of viral incubation, and thus increase the chance of
mosquitoes to become infected and infecting human with in their life span (Patz et
al., 1998). Precipitation contributes to the DF transmission via accumulation of rain
water which provides breeding grounds and stimulates egg hatching (Johansson et
al., 2009a). Higher levels of humidity are associated with an increase in biting
activity of mosquito (Rigau-Perez et al., 1998). Several studies have examined the
relationship between weather variability and DF epidemics and suggested that global
climate change will worsen the DF transmission (Hales et al., 2002, Hales et al.,
1999, Halide and Ridd, 2008, Thammapalo et al., 2005a). However, the magnitude of
the association between weather variability and DF fever varied with geographical
location and socio-environmental conditions (Thammapalo et al., 2007, Arcari et al.,
2007).
Chapter 1: Introduction 3
Mathematical modelling has been recently used to measure and predict the
impact of climate variation due to global climate change on DF transmission and
significant advances have been achieved in modelling approaches (McMichael et al.,
2006, Hu et al., 2010). Many countries around the world have developed different
models to predict the future distribution of DF in response to climate change (Hales
et al., 2002, Halide and Ridd, 2008, Johansson et al., 2009a, Johansson et al., 2009b).
Such projections can help to combat the increased risk of DF due to climate change
by taking necessary adaptation measures. However, only two studies have been
undertaken to identify the association between weather factors and DF transmission
in Bangladesh (Karim et al., 2012, Hashizume et al., 2012) and long term scenario-
based projections are yet to be developed. Therefore, it is important to further
examine the relationship between climate factors and DF in this location and develop
a better predictive model based on regional climate change scenarios.
Geographic information systems (GIS) have been widely used in vector-borne
disease epidemiology. GIS can visualize the spatial variation in disease risk and can
be used for disease mapping. Monitoring the space-time trends in disease occurrence
can highlight the changing patterns in risk and help to identify new risk factors
(Robertson and Nelson, 2010). Scan statistics are one of the most commonly used
approaches in spatial disease surveillance to explore high risk areas (Abrams and
Kleinman, 2007). Spatial analyses were used to identify the spatiotemporal pattern
and risk factors of DF transmission like socio-demographic and environmental
factors at both regional and country levels (Thai et al., 2010, Tran et al., 2004, Wen
et al., 2006, Wu et al., 2009, Mammen et al., 2008, Vanwambeke et al., 2006).
However, the trend of DF incidence and high risk areas for DF transmission at a
continental level remain to be determined.
4 Chapter 1: Introduction
1.2 RESEARCH AIMS
Given the limited knowledge on spatial distribution of DF and the imperative
to increase our knowledge on the impact of climate change on DF transmission, the
aims of this thesis are:
• To update and extend the current knowledge on the impact of climate
change and socio-environmental factors on DF transmission in the
Asia-Pacific region.
• To explore the spatiotemporal pattern and the high risk areas for DF
transmission in the Asia-Pacific region.
• To investigate the spatiotemporal distribution and to identify the high
risk areas for DF transmission in Bangladesh.
• To examine the association between climatic factors and DF
transmission in Bangladesh.
• To project the future risk of DF in Bangladesh based on different
climate change scenarios.
1.3 THESIS STRUCTURE
This thesis is presented in the conventional publication style, which consists of
four papers: Systematic Review, Results Paper 1, Results Paper 2 and Results Paper
3. Each paper was written for a particular journal. As each paper was designed to
stand alone, there was an inevitable degree of repetitiveness in their introduction,
methods and discussion sections. Chapter 2 presents the current knowledge on the
topic and critically reviews the relevant literature.
The four papers are presented in Chapter 2 through Chapter 5. Chapter 2
includes a systematic review of the related literature on climate change and DF in the
Asia-Pacific region which has been published in Tropical Medicine and
International Health (volume 16, issue 5, pages 598-607).
Chapter 1: Introduction 5
Chapter 3 (i.e., Results Paper 1) explored the spatial temporal pattern of DF
transmission in the Asia-Pacific region during 1955-2004. This paper has been
provisionally accepted by PLoS ONE.
Chapter 4 (i.e., Results Paper 2) examined the space-time distribution of DF in
Bangladesh during 2000- 2010 and identified the high risk cluster areas using scan
statistics. This paper has been published in Tropical Medicine and International
Health, (volume 17, issue 9, pages 1086-1091).
Chapter 5 (i.e., Results Paper 3) assessed the potential impact of climate
variability on the transmission of DF in Dhaka, Bangladesh and projected the future
DF risk based on different climate change scenarios. This paper has been accepted
for publication in Environment International.
Chapter 6 summarised the study findings across the three results papers and
discussed conclusions in relation to the overall aims of this study. This chapter
further discussed the study strengths, limitations, directions for future research and
public health implication of the research findings.
The references for each paper are presented at the end of their corresponding
chapter. A complete reference list (including all references cited in the manuscripts)
is provided at the end of the thesis.
Chapter 2: Literature Review 7
Chapter 2: Literature Review
This chapter presents a summary of the current knowledge on climate and dengue
research worldwide. It first describes the characteristics and ecology of DF and then
discusses the geographic distribution and the determinants of DF transmission followed
by a systematic review on the DF and global climate change. The final part of this
chapter is an update of the systematic review and list of knowledge gaps identified
through this literature review.
2.1 CHARACTERISTICS AND ECOLOGY OF DF
DF is the rapidly spreading vector borne disease endemic in tropical and
subtropical parts of the world (WHO, 2012a). More than 50% of the world’s population
live in over 100 countries are currently at risk of DF. Up to 100 million infections occur
annually and 22,000 children die due to DF globally (Guzman et al., 2010, Beatty et al.,
2011, Gibbons, 2010, Brady et al., 2012). The estimated number of disability-adjusted
life years (DALYs) is 1300 per million populations for the countries in Asia and
America (Guzman et al., 2010). The overall cost of a DF case was US$ 828 estimated
by a study in eight countries (Suaya et al., 2009). Thus, the aggregated annual economic
cost of DF was US$ 440 million during 2001-2005 in those countries (Suaya et al.,
2009). In America, the estimated cost is around 2.1 billion US dollars per year (Shepard
et al., 2011).
8 Chapter 2: Literature Review
DF is caused by one of the four serotypes of dengue virus which belong to the
genus flavivirus. Virus transmission occurs from viraemic to susceptible human mainly
by bites of Aedes aegypti and Aedes albopictus mosquitoes (Simmons et al., 2012,
Gibbons, 2010). Aedes aegypti is well established in most tropical and subtropical parts
of the world and is the principal vector for DF transmission. The Aedes albopictus is the
secondary vector of DF and continue to spread to new geographic areas of the tropical
and temperate climate (Wilder-Smith and Gubler, 2008). Typical disease manifestations
of DF range from an influenza like illness known as dengue fever (DF) to a severe
disease characterised by haemorrhage and shock, known as dengue hemorrhagic fever
(DHF) or dengue shock syndrome (DSS) (Gibbons, 2010, Guzman et al., 2010, Gubler,
2004b).
Figure 2.1 Transmission cycle of dengue fever
Chapter 2: Literature Review 9
DF transmission is most common in urban areas as Aedes aegypti is a highly
domesticated mosquito and thrives in crowded cities (Wu et al., 2009, Gubler, 2011,
Gubler, 2004b). Transmission occurs when a female mosquito bites an infected
individual during the viraemic phase of the disease; the mosquito then becomes
infective after an incubation period of approximately 10 days (Guzman et al., 2010,
Tran and Raffy, 2006). At this stage the virus is capable of being transferred to a human
host when the mosquito probes the skin. After that, the mosquito remains infective for
the rest of its life. Aedes aegypti mosquitoes are most active during the day, with
highest levels of activity occurring during early morning and evening (Chowell et al.,
2007, Lyerla et al., 2000). The ideal temperature range for the transmission of DF is 18
to 33.2°C, with females feeding more frequently when temperatures are higher (Epstein,
2001). The risk of transmission is determined by a multitude of different climatic,
household and individual risk factors (Mackenzie et al., 2004). Transmission is also
higher in areas where two or more serotypes circulate simultaneously (Wilder-Smith
and Gubler, 2008, Rigau-Perez et al., 1998). Recovery from infection by one dengue
serotype provides lifelong immunity against that serotype, but not to the other serotypes
(Simmons et al., 2012, Gibbons, 2010, Guzman et al., 2010).
2.2 DETERMINANTS OF DF TRANSMISSION
There are many factors such as virus, vector, host, and environment that are
involved in the transmission cycles of DF and making its ecology complex. Weather
variation, virus strain, mosquito densities, survival and breeding, human activities and
movement, socioeconomic status, and population immunity, all contribute to the
transmission of DF (Gubler, 2011). Uncontrolled urbanization and concurrent
population growth have resulted in substandard housing and inadequate water, sewer
and waste disposal systems, all of which increase mosquito breeding. Thus, the
increased human populations living in intimate contact with increasingly high densities
of mosquito populations create ideal conditions for increased DF transmission (Ansari
and Shope, 1994, Sukri et al., 2003, Wilder-Smith and Gubler, 2008). Tourism and
travel have also become important mechanisms for facilitating the dispersion of DF and
10 Chapter 2: Literature Review
its vectors (Rigau-Perez et al., 1998, Wilder-Smith and Gubler, 2008, Shang et al.,
2010, Tatem et al., 2006). The reinfestation of a region with Aedes aegypti or
introductions of new serotype are clear precursors of increased transmission. New viral
strain resulting from genetic changes and presence of multiple serotypes also increase
the risk of DF outbreak (Gubler, 1998, Leitmeyer et al., 1999, Rigau-Perez et al., 1998,
Simmons et al., 2012).
Figure 2.2 Determinants of dengue transmission (Sutherst, 2004)
Chapter 2: Literature Review 11
Age of the population and their race can also be important determinants of DF
infection. It appears that young children and older adults are at higher risk of the
development of more severe DF infection (Guzman et al., 2002, Ashford, 2003, Lee et
al., 2006). It has also been reported that white persons are at higher risk of developing
DF compared to black among those with a history of asthma, diabetes, renal
insufficiency and hypertension (Sierra et al., 2007). A recent study in Thailand showed
that changes in population age structure might explain the changes in DF incidence in
absence of other demographic changes (Cummings et al., 2009, Simmons and Farrar,
2009).
Climate is an important determinant of temporal and spatial distribution of DF
vector and its pathogen (Kovats et al., 2001, Lafferty, 2009). Climate variability and
extreme events can influence the growth of the virus and mosquito as well as people’s
behaviour and therefore affect the pattern of DF transmission (Koopman et al., 1991, Bi
et al., 2001, Thai and Anders, 2011).
2.3 EMERGENCE AND GEOGRAPHIC SPREAD OF DF
DF is an old disease and documented as early as 1635 and 1699 in West Indies
and Central America, respectively (Wilder-Smith and Gubler, 2008, Mackenzie et al.,
2004). Even though the geographic origin of DF is still uncertain, the presence of all
viral serotypes in human and monkeys from Asia and the phylogenetic position of the
Asian sylvatic strain indicate the possible origin of DF in Asia rather than in Africa.
In Southeast Asia, DF has rapidly emerged as a public health concern after the
World War II (Gubler, 2011, Kyle and Harris, 2008). The major ecological and
demographic changes that occurred due to the war might facilitate the geographic
expansion of dengue viruses and the vectors. The economic development, unplanned
urbanization, modern transportation and lack of mosquito control activities after the war
were also responsible for the dramatic increase of DF epidemic in this area during
12 Chapter 2: Literature Review
1950s and 1960s (Wilder-Smith and Gubler, 2008, Kyle and Harris, 2008, Tatem et al.,
2006, Mackenzie et al., 2004).
Figure 2.3 Countries at risk of DF transmission, 2008 (Source: World Health Organization, http://www.nathnac.org/pro/factsheets/images/clip_image002_015.jpg)
Before 1981, DF outbreaks in the American tropics were self-limiting due to the
presence of single serotype only (Guzman and Istúriz, 2010). However, the introduction
of new serotypes from Southeast Asia to the Caribbean, the South Pacific Islands and
the America during 1980s and 1990s made this region hyper endemic (presence of
multiple serotypes) and severe DF epidemic became frequent (Wilder-Smith and
Gubler, 2008, Gubler, 2011, Guzman and Istúriz, 2010). Imported DF is the most
common cause of febrile illness in returned travellers in Europe and Aedes albopictus
has already established in European countries including Albania, Italy, France, Spain,
Switzerland, Slovenia, Croatia, Bosnia and Herzegovina, Greece and Montenegro with
Chapter 2: Literature Review 13
increasing risk of DF transmission (Guzman and Istúriz, 2010, Semenza and Menne,
2009, Gasperi et al., 2012). The high adaptive capability of this species due to cold
hardiness, egg diapauses and the ability to mature eggs without a blood meal permits
them to spread and successfully establish in both tropical and temperate regions
(Gasperi et al., 2012, Delatte et al., 2009). In 2010 local DF transmissions were
documented in France and Croatia (WHO, 2012a). It has been predicted that the current
geographic range of Aedes albopictus might expand to new areas due to climate change
(Fischer et al., 2011, Caminade et al., 2012).
In the past 35 years, there has been a remarkable global re-emergence of DF
epidemic and the virus and its vectors expanded into new areas (Gubler, 2004a,
Mackenzie et al., 2004). More than 120 tropical countries are now endemic with DF
and the reported number of DF cases increased by fivefold over the last three decades
(Brady et al., 2012, Kyle and Harris, 2008). Between 2000 and 2007 at least eight
previously DF free countries became infected. Outbreaks of suspected DF were
recorded in Pakistan, Saudi Arabia, Yemen, Sudan and Madagascar during 2005 and
2006 (Guzman and Istúriz, 2010). However, the reason for this global pandemic is not
fully understood and there has been lack of empirical evidence about the spatial
temporal pattern of DF globally (Cummings et al., 2004, Wilder-Smith and Gubler,
2008).
2.4 CLIMATE VARIABILITY AND DF
DF is present either in epidemic cycles or seasonally which suggests a correlation
between climate and pattern of DF (Reiter, 2001). Rainfall, temperature and relative
humidity are thought as important factors attributing towards the growth and dispersion
of mosquito vector and potential of DF outbreaks (Chaturvedi et al., 2006, Chowell and
Sanchez, 2006, Ram et al., 1998, Wu et al., 2007). Temperatures influence the life cycle
of Aedes aegypti, including growth rate and survival of larval stage, time to first blood
meal, and the length of gonotropic cycle (Patz et al., 2005). Temperature is also capable
14 Chapter 2: Literature Review
of affecting pathogen replication, maturation and period of infectivity. Higher
temperatures shorten the incubation period of the dengue virus, thus increasing the
proportion of mosquitoes that are infectious at any given time (Patz et al., 1998).
The humidity of a region is also a potential risk factor for DF transmission. It has
been suggested that the relative humidity is related to the hatching and activities of
mosquito vectors, with higher levels of humidity associated with a decreased incubation
time and an increase in activity levels (Rigau-Perez et al., 1998). A study by Hales et al.
(2002) suggested that rather than relative humidity, mean annual vapour pressure was
highly correlated with the incidence of DF. Precipitation has also been identified as an
important climatic risk factor contributing to the transmission of DF (Hales et al.,
2002). The accumulation of water that occurs by precipitation provides Aedes Aegypti
vectors with an increased number of available breeding grounds (Keating, 2001).
Observations have suggested that the greatest rate of increase in mosquito density is
associated with the onset of a rainy season, rather than the peak (Nagao et al., 2003).
El Niño/Southern Oscillation (ENSO) is a periodic variation in the atmospheric
condition and ocean surface temperatures of the tropical Pacific, which affects most
countries of the Pacific and Indian Oceans, bringing long drought and wet periods every
2-7 years (Hales et al., 1999). ENSO has an important influence on the timing and inter-
annual variability in DF transmission as it causes variation in local temperature and
precipitation. (Johansson et al., 2009a, Tipayamongkholgul et al., 2009, Cazelles et al.,
2005, Hales et al., 1999). A significant but non-stationary association between ENSO
and DF epidemic was observed in Thailand and Puerto Rico (Cazelles et al., 2005,
Johansson et al., 2009a). The non-stationary relationship implies that sometimes ENSO
plays role in the timing and synchrony of DF epidemic and sometimes it does not.
Chapter 2: Literature Review 15
2.5 GLOBAL CLIMATE CHANGE AND DF
Climate change is a change in the statistical properties of the climate system when
considered over long periods of time, regardless of cause (IPCC, 2012). There is strong
evidence indicating that climate change is occurring (IPCC, 2007). Increases in global
population and corresponding increases in demand for energy resources have caused a
radical increase in energy consumption resulting in an increase in greenhouse gas
emissions. As a result global temperature has increased by 0.5 °C over the last thirty
years (IPCC, 2007). The rate of climate change is now faster than it has been in the
previous 1000 years; modelling has predicted that global temperatures will rise a further
1.5 – 4.5°C by 2100 (IPCC, 2007). As global temperatures continue to increase, it is
predicted that the endemic range of DF will expand geographically (Githeko et al.,
2000, Hopp and Foley, 2001, McMichael et al., 2006, Sutherst, 2004, Woodruff and
McMichael, 2004). Warmer temperatures will also allow for increased reproduction and
activity and decreased incubation time of larvae, resulting in an increased capacity for
producing offspring. Thus an increase in the transmission potential and prevalence of
DF seems likely (Jetten and Focks, 1997, McMichael et al., 2006).
The increasing temperatures associated with climate change could also increase
DF transmission by extending the season in which transmission occurs (Patz and
Reisen, 2001). Lengthy drought conditions in endemic areas without a stable drinking
water supply may encourage the storage of drinking water, thereby increasing the
number of developmental sites for the primary vector Aedes aegypti (Patz et al., 1998).
Conversely, high rainfall would ensure that small artificial containers used as larval
mosquito habitat, would remain flooded, thereby expanding adult mosquito population
(Patz et al., 1998). A continuous inter-annual variability in ENSO has been projected by
IPCC (IPCC, 2007). Local climate changes associated with ENSO may trigger an
increase in DF transmission in populated areas where the disease is endemic (Hales et
al., 1999). As climate change may have a significant impact on the transmission and
incidence of DF a better understanding of the association between climate and DF
transmission is imperative (Chan et al., 1999, Racloz et al., 2012).
16 Chapter 2: Literature Review
Systematic review
Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors
Published in of Tropical Medicine and International Health
Institute for Scientific Information (ISI) Impact Factor: 2.8
Citation:
BANU, S., HU, W., HURST, C. & TONG, S. 2011. Dengue transmission in the
Asia-Pacific region: impact of climate change and socio-environmental factors.
Tropical Medicine and International Health, 16, 598-607.
Authors Contribution:
Shahera Banu performed all literature searches and wrote the manuscript. Shilu
Tong and Wenbiao Hu supervised the study and assisted with writing the manuscript.
Cameron Hurst contributed to the manuscript in terms of providing feedback on initial
drafts.
Chapter 2: Literature Review 17
2.6 SUMMARY
OBJECTIVE: To review the scientific evidence about the impact of climate
change and socio- environmental factors on dengue transmission, particularly in the
Asia-Pacific region.
METHODS: A search of the published literature on PubMed, ISI web of
Knowledge and Google Scholar was conducted. Articles were included if an association
between climate or socio-environmental factors and dengue transmission was assessed
in any country of the Asia-Pacific region.
RESULTS: A total of twenty-two studies met the inclusion criteria. The weight of
the evidence indicates that global climate change is likely to affect the seasonal and
geographic distribution of dengue fever (DF) in the Asia-Pacific region. However,
current empirical evidence linking DF to climate change is inconsistent across
geographic locations and absent in some countries where DF is endemic.
CONCLUSION: Even though climate change may play an increasing role in the
transmission of DF, no clear evidence shows that such impact has already occurred.
More research is needed across countries to better understand the relationship between
climate change and DF transmission. Future research should also consider and adjust
for the influence of important socio-environmental factors in the assessment of the
climate change-related effects on DF transmission.
18 Chapter 2: Literature Review
2.7 INTRODUCTION
Global climate is changing rapidly, due primarily to anthropogenic greenhouse
gas emissions (IPCC, 2007). Global mean temperature is projected to increase between
1.1–6.4oC by the end of this century relative to 1980-1999. There is growing evidence
that climate change has already affected, and will increasingly impact on human health
(Epstein, 2005, Haines et al., 2006, Costello et al., 2009, McMichael et al., 2006). The
changes in global temperature, precipitation and humidity that are expected to occur
due to projected climate change will affect the biology and ecology of disease vectors
and consequently the risk of vector-borne disease transmission (Gubler et al., 2001,
Tong et al., 2008, Shuman, 2010). Dengue fever (DF) has been recognised as the most
important arboviral disease in the world (Gubler, 2002a, WHO, 2006). About 2.5
billion people live in endemic areas and an additional 120 million people travel to
affected areas every year. The annual number of DF infections is estimated to be 50 to
100 million. Case fatality rates vary between 0.5%–3.5% in Asian countries (Guzman
and Kouri, 2002, Suaya et al., 2009, Halstead, 2007). Cases reported to the World
Health Organization (WHO) over the past four decades show an upward trend,
especially in urban areas (WHO, 2006).
Climate variability, lack of effective vector control programs, uncontrolled
urbanization and increased international travel are suggested to be the important risk
factors for increased activity of DF (Sutherst, 2004, Gubler, 2002a). Temperature,
rainfall and relative humidity are thought as important climatic factors contributing
towards the growth and dispersion of the mosquito vector and potential of DF outbreaks
(Patz et al., 2005). Temperature is also capable of affecting pathogen replication,
maturation and period of infectivity (Patz et al., 1998).
Chapter 2: Literature Review 19
As global temperatures continue to increase, it has predicted that the endemic
range of DF will expand geographically (Hopp and Foley, 2001, Githeko et al., 2000,
Woodruff and McMichael, 2004). Warmer temperatures will also allow for increased
reproduction and activity and decreased incubation time of larvae, resulting in an
increased capacity for producing offspring. Thus an increase in the transmission
potential and prevalence of DF seems likely (Jetten and Focks, 1997, Barbazan et al.,
2010). The increasing temperatures could also increase DF transmission by extending
the season in which transmission occurs (Patz and Reisen, 2001). Lengthy drought
conditions in endemic areas without a stable drinking water supply may encourage the
storage of drinking water, thereby increasing the number of breeding sites for the
mosquito vector Aedes aegypti (Beebe et al., 2009). Conversely, high rainfall will
ensure that small artificial containers used as larval mosquito habitat, would remain
flooded, thereby expanding adult mosquito population (Patz et al., 1998). Moreover,
climate change associated with El Niño-Southern Oscillation (ENSO) may trigger
outbreak of DF in populated areas where the disease is endemic (Hales et al., 1999,
Johansson et al., 2009b).
As climate change may have a significant impact on the transmission and
incidence of DF, it is essential to examine the association between climatic variables
and DF epidemics. Recently a few studies have explored the impact of climate
variability and climate change on DF transmission (Hales et al., 2002, Johansson et al.,
2009a). In addition, several studies have assessed the influence of socio-ecological
factors on DF epidemics (Cummings et al., 2009, Johansson et al., 2009b, Bohra and
Andrianasolo, 2001, McBride et al., 1998, Thammapalo et al., 2005a). However, the
link between DF and climate change remains inconclusive because the potential
influence of socio-demographic factors on DF transmission has rarely been considered
seriously in previous research (Patz et al., 1998, Reiter, 2001, Semenza and Menne,
2009). In this paper, we reviewed the reported effects of climate change and other
socio-environmental factors on DF transmission in the Asia-Pacific region to draw
20 Chapter 2: Literature Review
attention to the methodological challenges in this area. Additionally, we provided
recommendations for future research directions.
2.8 METHODS
A literature search using PubMed, ISI Web of Knowledge and Google scholar
was conducted in December 2009. Keywords such as ‘dengue , ‘dengue fever’ ,
‘dengue hemorrhagic fever’, ‘climate change*’ , ‘climate variability’ , ‘climate
model*’, ‘Risk factors’ and ‘socio-environmental factors’ were used in different
combination to identify potential articles and references. Search terms included MeSH
terms and free text terms. Limits were set for language (English) and publication year
(1990 to 2009). In Google Scholar, we limited our search by subject (Biology, Life
Science and Environmental science) as it gives huge number of irrelevant references.
However, we searched all fields for PubMed and Web of Science. Table 2.1 shows the
search results from each database. The initial search generated 1931 references
including duplicates. All titles and 466 abstracts were reviewed to identify potential
epidemiological studies. Then 290 full text articles were retrieved based on abstracts
reviewed and critically analysed by the first author.
Chapter 2: Literature Review 21
Table 2.1 Search strategy
Search terms Pub Med ISI Web of Knowledge
Google Scholar
Total (including duplicates)
# 1 ('Dengue' OR 'Dengue fever' OR 'Dengue Hemorrhagic fever') AND ('climate change*' OR ' climate variability' OR 'climate model*')
310 134 167 611
# 2 ('Dengue' OR 'Dengue fever' OR 'Dengue Hemorrhagic fever') AND ('Risk factors' OR 'Socio-environmental factors')
325 216 584 1125
#1 AND #2 ('Dengue' OR 'Dengue fever' OR 'Dengue Hemorrhagic fever') AND ('climate change*' OR ' climate variability' OR 'climate model*') AND ('Risk factors' OR 'Socio-environmental factors')
47 19 129 195
Total (including duplicates) 1931
22 Chapter 2: Literature Review
2.8.1 Inclusion criteria
Articles were included in the review if they (a) evaluated the effects of climate
variability or the influence of other socio-environmental factors on DF transmission (b)
conducted in countries of the Asia-Pacific region (c) used an observational study
design. Only studies conducted in the Asia-Pacific region were included because many
countries of this region are prone to DF transmission but no systematic review has
focused on this region in the literature yet. Observational study designs included time
series analysis, spatial or spatiotemporal analysis and descriptive study which applied to
identify the influence of climate variables (temperature, humidity, precipitation, wind
speed, El Niño events) and socioenvironmental factors (population density, urbanizatio
n, housing, family income, transport, vector control) on DF transmission.
Chapter 2: Literature Review 23
Figure 2.4 Flow diagram of article selection process
22 articles found potentially relevant
18 modelled climatic factors
4 modelled environmental factors except climate variables
Inclusion criteria used were:
• From the Asia –Pacific region • Reports association between DF and climatic or
non-climatic factors • Observational study design • Published in English
1931 references were found after searching PubMed, ISI Web of knowledge and Google scholar databases
466 abstracts were read and 290 full text articles were retrieved
268 articles rejected due to irrelevant research question and non-observational study design
1465 references rejected (studies describing virological or serological features of DF and treatment or vaccine development)
24 Chapter 2: Literature Review
2.9 RESULTS
Among 290 full text articles, 22 articles met the inclusion criteria and the major
findings of these studies were reviewed and summarized in Table 2.2 and Table 2.3. All
articles included in this review were published between 1998 and 2010. Eight studies
from Thailand, three each from Australia, Taiwan and Indonesia, two each from China
and India and one from Singapore. There was no study conducted in some countries of
the Asia-Pacific region where DF is endemic like Bangladesh, Sri Lanka, and
Myanmar.
2.9.1 Association between climatic factors and DF
Out of 22 published studies, eighteen evaluated the relationship between climatic
variables and DF incidence (Arcari et al., 2007, Bangs et al., 2006, Cazelles et al., 2005,
Chakravarti and Kumaria, 2005, Halide and Ridd, 2008, Hii et al., 2009, Hsieh and
Chen, 2009, Johansson et al., 2009b, Lu et al., 2009, Nagao et al., 2003, Nakhapakorn
and Tripathi, 2005, Thammapalo et al., 2005b, Tipayamongkholgul et al., 2009, Wu et
al., 2007, Yang et al., 2009b, Bi et al., 2001, Wu et al., 2009, Hu et al., 2010). Among
them, only three studies considered both climatic and social factors (Wu et al., 2009,
Nagao et al., 2003, Nakhapakorn and Tripathi, 2005). The main outcome variables
assessed in these studies were the number of DF cases and/ Aedes mosquito density.
Most studies collected data on temperature, amount of precipitation and relative
humidity as climatic variables (Cazelles et al., 2005, Nakhapakorn and Tripathi, 2005,
Thammapalo et al., 2005b, Wu et al., 2007). Several used El Niño -Southern Oscillation
(ENSO) as an independent variable (Johansson et al., 2009a, Tipayamongkholgul et al.,
2009, Cazelles et al., 2005). Only one study assessed the influence of extreme weather
event (Typhoon) on DF (Hsieh and Chen, 2009).
Chapter 2: Literature Review 25
Table 2.2 Characteristics of studies on the association between climatic variables and DF transmission
Study Location (study period)
Statistical method
Risk factors Major findings Comments
Johansson et al. 2009
Thailand (1983-1996)
Wavelet analysis
ENSO Precipitation & temperature
The direct relationship between ENSO and DF was nonstationary and ENSO appeared to be associated with local temperature and precipitation.
Further research is needed to explain the nonstationarity in ENSO and DF incidence.
Tipayamongkholgul el al. 2009
Thailand (1996-2005)
Poisson regression
ENSO Temperature Relative humidity Wind speed
The effects of El Niño on DF transmission were varied according to geographical location within Thailand.
Only southern coastal and northern inland regions were included
Cazells et al. 2005 Thailand (1983-1997)
Wavelet analysis ENSO
The dynamics of El Niño were strongly associated with DF incidence but nonstationary existed in this relation which might influenced by the synchrony of previous DF epidemic.
The magnitude of the El Niño and DF relationship needs to be viewed within a wider context of socio-environmental variability.
Nakhapakorn and Tripathi 2005
Thailand (1997-2001)
Multiple regression
Rainfall Temperature Humidity Land use
Built-up area had highest risk of DF incidence and temperature, rainfall and humidity were likely to be key determinants of DF
Only one province was included
Thammapalo et al. 2005b
Thailand (1978-1997)
Linear least square regression
Monthly total rainfall Rain days Daily temperature Daily relative humidity
Increased temperature was positively associated with DF incidence in central and northern parts of Thailand where increased rainfall was negatively associated in southern Thailand.
Spatial variation was not examined
26 Chapter 2: Literature Review
Table 2.2 (continued)
Study Location (study period)
Statistical method Risk factors Major findings Comments
Nagao et al. 2003
Thailand (1992-1996)
Multiple regression
Temperature, Rainfall Water wells Tin houses
Larval abundance of Aedes mosquito was positively associated with house conditions, water supply and transport services. Increased rainfall in 2 months earlier and temperature were also correlated to larval indices.
Only 18 province of Northern Thailand were included
Hsieh and Chen 2009 Taiwan (2007)
Richards model Distributed lag model
Typhoon Temperature Precipitation
The multi-wave DF outbreak in Taiwan in 2007 was appeared to be influenced by rainfall and temperature variation due to two consecutive typhoons.
Further research is required to explore the relationship between extreme weather events and DF transmission
Wu et al. 2007 Taiwan (1998-2003)
ARIMA model Temperature Rainfall Relative humidity Vector density
The incidence of DF was negatively associated with monthly temperature variation and reversely with relative humidity at lags of 2 months.
Only one metropolitan city was included
Wu et al. 2009 Taiwan (1998-2002)
Spatial analysis Logistic regression
Temperature Rainfall Level of urbanization Percentage of elder population
Number of warm months and degree of urbanization were found to be associated with increasing risk of DF incidence at township level
Both climatic variables and socio-demographic factors were considered.
Chapter 2: Literature Review 27
Study Location (study period)
Statistical method Risk factors Major findings Comments
Halide and Ridd 2008
Indonesia (1998-2005)
Multiple regression
Temperature Relative humidity Rainfall
Relative humidity at 3-4 months lags and current number of DF cases appeared to be major determinants for prediction of DF outbreak in Indonesia.
Only one city was included
Arcari et al. 2007 Indonesia (1992-2001)
Multiple regression Temperature Rainfall Relative humidity SOI
Rainfall and temperature observed as important predictor of DF transmission in Indonesia, although the association differed across geographic region of the country.
Socio-environmental factors were not included
Bangs et al.2006 Indonesia (1997 &1998)
Descriptive analysis
Temperature Relative humidity Rainfall, ENSO House index
ENSO driven increased temperature exhibited greater impact on DF transmission by the vector population.
Only one city was included
Chakravarti and Kumaria 2005
India (2003)
Descriptive analysis
Temperature Rainfall Relative humidity
Temperature, rainfall and relative humidity were major determinants for DF transmission and outbreak coincided in post monsoon period.
Only one outbreak in one city was considered.
Table 2.2 (continued)
28 Chapter 2: Literature Review
Study Location (study period)
Statistical method Risk factors Major findings Comments
Lu et al. 2009
China (2001-2006)
Poisson regression
Temperature, rainfall Relative humidity Wind velocity
Increase minimum temperature and decreased wind velocity were associated with increase DF incidence.
Only one province was included
Yang et al. 2009 China (July –October 2004)
Descriptive analysis
Temperature, Precipitation Humidity Breteau index House index
DF incidence seemed to have no relationship with climatic factors. However, negatively associated with vector control.
Non endemic nature of DF in Cixi city may biased the relationship between climate variables and DF
Hii et al. 2009 Singapore (2000-2007)
Poisson regression Temperature Precipitation
Weekly mean temperature and total precipitation were related to increased DF incidence.
Socio-environmental factors were not included
Hu et al. 2010 Australia (1993-2005)
SARIMA model ENSO Decreased mean of SOI at lags of 3-12 months earlier was inversely associated with DF incidence in Queensland.
Only SOI was considered
Bi et al. 2001 Australia (1990-1994)
ARIMA model Temperature Precipitation Relative humidity
Monthly mean minimum temperature was the major contributor for DF outbreak in Townsville.
Only four years dataset used
Abbreviations: ENSO= El Niño-Southern Oscillation Index; ARIMA= Autoregressive Integrated Moving Average; SARIMA= Seasonal Autoregressive
Integrated Moving Average; SOI= Southern Oscillation Index.
Arranged by location of study
Chapter 2: Literature Review 29
Different methods were used to evaluate the association between climatic
variables and DF incidence or mosquito density. Of the 18 studies reviewed, two
used spatial analyses (Wu et al., 2009, Nakhapakorn and Tripathi, 2005); thirteen
time series analyses (Arcari et al., 2007, Bi et al., 2001, Cazelles et al., 2005,
Halide and Ridd, 2008, Hii et al., 2009, Johansson et al., 2009a, Lu et al., 2009,
Thammapalo et al., 2005b, Tipayamongkholgul et al., 2009, Wu et al., 2007, Hu
et al., 2010, Hsieh and Chen, 2009, Nagao et al., 2003) and three descriptive
analyses (Bangs et al., 2006, Chakravarti and Kumaria, 2005, Yang et al., 2009b).
Various multiple regressions analyses like logistic regression and Poisson
regression were mostly used to analyse and predict DF transmission. These time
series regression analyses provide a way to establish the relationship between
changes of weather parameters, environmental factors and the occurrence of DF
cases which might be utilized for forecasting future DF outbreak based on model
scenarios (Wu et al., 2007). Both classical autoregressive integrated moving
average (ARIMA) model and seasonal ARIMA model were also applied to link
between weather variation and DF (Bi et al., 2001, Wu et al., 2007, Hu et al.,
2010). Spatial analysis of DF occurrence was used to identify the high risk area
for DF outbreaks (Wu et al., 2009). However, none of the previous studies
included both spatial and temporal data into their models. The identification of
high risk areas through spatiotemporal modelling may assist existing surveillance
and control efforts by permitting limited resources in areas where DF outbreaks
are most likely to occur.
Association between climatic factors and DF incidence or mosquito density
was reported in most studies (Arcari et al., 2007, Bangs et al., 2006, Bi et al.,
2001, Cazelles et al., 2005, Chakravarti and Kumaria, 2005, Halide and Ridd,
2008, Hii et al., 2009, Hsieh and Chen, 2009, Johansson et al., 2009a, Lu et al.,
2009, Nagao et al., 2003, Nakhapakorn and Tripathi, 2005, Thammapalo et al.,
2005b, Tipayamongkholgul et al., 2009, Wu et al., 2007, Hu et al., 2010).
However, the direction and intensity of this association varied by time and
30 Chapter 2: Literature Review
location (Thammapalo et al., 2005b, Tipayamongkholgul et al., 2009, Arcari et
al., 2007). Temperature, rainfall and relative humidity were often found as major
determinants of DF transmission. There was no association between DF incidence
and climate variability in Cixi, China, which might due to the non-endemic nature
of DF in Cixi and the short study period (four months) (Yang et al., 2009b).
Several non-climatic factors such as urbanization, land use, vector-control
program, human movement, housing quality and population immunity were
identified as potential confounders for assessing the climate-DF relationship.
Among 18 studies reviewed, only one study was adjusted for socio-environmental
factors (Nagao et al., 2003). It was reported that the lack of relevant dataset makes
it difficult to control these variables (Wu et al., 2007, Arcari et al., 2007, Bi et al.,
2001).
Most studies examining the temperature influence on DF transmission or
mosquito density reported a positive association (Hii et al., 2009, Nagao et al.,
2003, Arcari et al., 2007, Thammapalo et al., 2005b, Lu et al., 2009, Bi et al.,
2001, Wu et al., 2009). Higher temperature favours virus replication, vector
proliferation and feeding frequency of mosquito (Patz et al., 1998, Jetten and
Focks, 1997). Therefore, rising temperature may enhance DF transmission.
However, the impact of increased temperature on DF outbreak was not
immediate. Various lag times were reported for this relationship, ranging from 4–
16 weeks (Arcari et al., 2007, Bi et al., 2001, Hsieh and Chen, 2009, Lu et al.,
2009, Wu et al., 2007, Hu et al., 2010). Bi et al. 2001 found that monthly mean
minimum temperatures impacted on the transmission of DF through a four month
lagged period which includes the period of replication and development of
mosquito, the extrinsic incubation period (time of replication of virus within
vector) and intrinsic incubation period of the virus (time of virus replication
within the host)(Bi et al., 2001). Therefore, observed lags were biologically
plausible.
Chapter 2: Literature Review 31
Associations between rainfall and DF transmission are inconsistent across
geographical locations. DF outbreaks usually coincided with wet season and
positive associations between DF incidence and precipitation were reported in
many countries of the Asia-pacific region (Nagao et al., 2003, Nakhapakorn and
Tripathi, 2005, Arcari et al., 2007). Rainfall may increase the breeding sites of
Aedes mosquito and therefore, increase DF transmission. However, excess rainfall
was found negatively associated with DF incidence in Thailand, Indonesia and
Taiwan (Thammapalo et al., 2005b, Halide and Ridd, 2008, Wu et al., 2009). The
plausible explanation might be the mosquito larvae were washed away by heavy
rainfall (Arcari et al., 2007, Thammapalo et al., 2005b).
The El Niño Southern Oscillation (ENSO) is a periodic variation in the
atmospheric conditions and ocean surface temperatures of the tropical Pacific and
hypothesized to have influence on multiyear variation in DF incidence (Johansson
et al., 2009a). Among the studies reviewed, four examined the impact of ENSO
on the synchrony of DF epidemics in Thailand and Australia (Cazelles et al.,
2005, Johansson et al., 2009a, Tipayamongkholgul et al., 2009, Hu et al., 2010).
They reported a significant but non-stationary influence of ENSO on DF
transmission (Cazelles et al., 2005, Johansson et al., 2009a, Tipayamongkholgul
et al., 2009, Hu et al., 2010). Spatial heterogeneity and various lag effects were
also apparent in this relation (Tipayamongkholgul et al., 2009, Hu et al., 2010). In
Thailand, ENSO was associated with local temperature and precipitation changes.
It was suggested that decreased ENSO could result in increased temperature and
decreased rainfall leading to increased water storage, increased mosquito breeding
sites and therefore, increased DF transmission (Johansson et al., 2009a).
Moreover, further research is necessary to explain the non-stationarity and spatial
variation in the ENSO- DF relationship to effectively support the hypothesis of
inter -annual variation in DF transmission.
32 Chapter 2: Literature Review
2.9.2 Association between socio-environmental factors and DF
Four studies have examined the impact of socio-environmental factors on
DF incidence in the Asia-Pacific region (Table 2.3) (McBride et al., 1998,
Thammapalo et al., 2005a, Bohra and Andrianasolo, 2001, Cummings et al.,
2009). The increasing trends in population growth, uncontrolled urbanization,
spread of mosquito vector and movement of virus through international travel
were suggested to be the major contributing factors for the recent DF expansion
in endemic areas (Kyle and Harris, 2008, Campbell-Lendrum and Reithinger,
2002, Gubler et al., 2001). Influences of different socio-environmental factors on
DF transmission were illustrated below from a global perspective as very little
evidence is available from the Asia-Pacific region.
Socio-economic condition
DF transmission might be influenced by people’s socio-economic status
(Gubler et al., 2001, Reiter et al., 2003). People in developed countries usually
live in better houses with glazed windows, piped water, insect screening and air-
conditioning than those in developing countries (Reiter, 2001). These facilities
may effectively reduce their contacts with vector mosquitoes and even if infected
mosquitoes gain entry to these buildings, the low ambient temperature and
artificially dry atmosphere may decrease their survival rate and reduce the risk of
transmission (Reiter, 2001). Besides these, many activities, particularly social
gatherings which occur in outdoor situations such as balconies, courtyards and
outdoor restaurants might facilitate contact with vector (Reiter, 2001, Gubler et
al., 2001). It has been suggested that the large difference in disease incidence
between U.S. and Mexico Border States is probably caused by differences in
living standards and human behaviours (Gubler et al., 2001).
Chapter 2: Literature Review 33
A spatial analysis of DF in Thailand reported that the risk of DF was quite
geographically homogenous and associated with housing types and poor garbage
disposal (Thammapalo et al., 2007). Housing style was strongly related to the
number of water jars and discarded items. Town houses and slum houses in
Phuket had relatively low quantity of discarded wet containers than single houses
on rubber plantations. Therefore, single houses had 3-15 times higher risk of DF,
because these dwelling types have sufficient open space where discarded items
could be easily filled with rain water which can create mosquito breeding sites
(Thammapalo et al., 2005a).
International travel
International trade and transports were suggested to have potential influence
on the geographical distribution of vectors and pathogen (Gubler et al., 2001,
Reiter, 2001, Sutherst, 2004). Commercial shipping might be linked to the spread
of both A. aegypti and A. albopictus between regions (Romi et al., 1997). For
example, after the introduction of used tires, an American species of Aedes
albopictus has established in Italy (Romi et al., 1997). In addition, air-travel has
greatly increased the dissemination of dengue viruses via rapid transit of viraemic
individuals around the world (Kyle and Harris, 2008). As a result, the worldwide
movement of dengue virus has been greatly facilitated by air travel (Reiter, 2001).
Introduction of new dengue viruses by travellers was one of the important factors
for causing hyper-endemicity in Puerto Rico and increased severity of the disease
(Gubler and Trent, 1993, Gubler et al., 2001).
34 Chapter 2: Literature Review
Table 2.3 Characteristics of studies on the association between Socio-environmental factors and DF transmission
Arranged by location of study
Study Location (study period)
Statistical method Risk factors Major findings Comments
Cummings et al. 2009
Thailand (1980-2005)
Linear regression Wavelet analysis
Population data (age, birth rate, death rate, household size, education level, sanitation) Climatic data(rainfall)
Changes in the birth rate and death rate in Thailand can explain the changes in the age distribution of DF cases and its periodicity.
Both socio-demographic and climatic factors were considered.
Thammapalo et al. 2005a
Thailand (1999)
Logistic regression Housing style Window screen Ethnicity Education level Occupation Age, gender
Housing style, window screen and ethnicity seemed to affect the presence of Aedes mosquito larva in the house.
A complex relationship was revealed between Aedes larval abundance and socio- environmental factors
McBride et al. 1998
Australia (1993)
Logistic regression House hold cases, neighbour cases Window screen Travel history Mosquito control Presence of water tank
Viral serotypes, vector populations, unscreened houses and hard immunity emerged as important risk factors for DF increased DF incidence in Charter Tower in 1993.
Climatic factors were not included
Bohra and Andrianasolo 2001
India (1990)
Spatial analysis Multiple regression
Uncovered water container Presence of solid waste Mosquito control Housing pattern Water supply
Housing pattern, mosquito control facility, presence of solid waste and uncovered water container were most important socio-cultural risk factors for DF transmission.
Climatic factors were not considered.
Chapter 2: Literature Review 35
Public Health Intervention
The health risk arising from climate change may differ between countries
depending on the quality of public health infrastructures (Githeko et al., 2000). In
Canada and USA, good surveillance and vector control programs limited endemic
transmission of DF, whereas in Mexico and other less developed countries the health
infrastructures are still ineffective (Githeko et al., 2000). The majority of DF
endemic countries in Asia do not have laboratory based active surveillance system
that can provide accurate early warning for epidemic DF (Gubler, 2002b). In the
pacific, only Australia, Tahiti and New Caledonia have good laboratory surveillance
(Gubler, 2002b). Recently, there have been considerable improvements in
surveillance and vector control in Australia (Ritchie et al., 2002). Therefore, it seems
unlikely that DF will become re-established as an endemic disease in Australia
(McMichael, 2003).
2.10 CONCLUSION
The review indicates that global climate change is likely to affect the seasonal
and geographic distribution of DF in the Asia-Pacific region. The complex nature of
the DF transmission which involves vector ecology, viral factors, population
immunity and socio-demographic changes makes it difficult to quantify how climate
change affects DF transmission. However, the impact of socio-environmental
changes on DF transmission is less well studied. Therefore, relative importance and
interaction between socio-environmental and climatic factors remain to be elucidated
and need further exploration. Biological judgement and caution are also necessary in
interpreting a direct relationship between climatic factors or non-climatic factors and
DF transmission. We hope this review inspires further work to elucidate the relative
importance and interaction between climatic or non-climatic factors in the
transmission of DF.
36 Chapter 2: Literature Review
There is the lack of data in many countries where DF is endemic. In order to
demonstrate the regional variation in the climate-DF relationship, a multidisciplinary
approach incorporating region-specific predicted climate changes and non-climatic
factors could help to identify consistencies in interactions between DF and certain
climatic and non-climatic factors. Thus, a more sophisticated multivariable predictive
model may be constructed for control and prevention of DF. Furthermore, multi-
centre or multi-country studies using both climatic and socio-environmental data
could help to reveal the potential impact of climate change on DF transmission in the
Asia-Pacific region.
Funding
SB is funded by a Queensland University of Technology Postgraduate
Scholarship. ST is supported by an NHMRC Research Fellowship (# 553043). WH is
funded by NHMRC Postdoctoral Training Fellowship (#519788).
Conflict of interest
We declare that we have no conflict of interest.
Chapter 2: Literature Review 37
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40 Chapter 2: Literature Review
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42 Chapter 2: Literature Review
2.11 UPDATE OF THE SYSTEMATIC REVIEW
As the systematic review was completed in December 2009, an updated
literature search was conducted in February 2013. We applied similar methods and
inclusion criteria as the systematic review to search databases and select relevant
articles. However, we modified the search terms by including weather, temperature,
rainfall and humidity in the keywords. The literature search was limited by
publication year (January 2010 to February 2013) to avoid duplication. A total of
twenty four articles met the inclusion criteria and included in the updated literature
review.
2.11.1 Association between climatic factors and DF
Of the 24 articles that met the inclusion criteria, 20 studies evaluated the
relationship between climatic factors and DF incidence. Time series analyses were
mostly used to explore the association between climate and DF and only two studies
used spatial analysis techniques. Among time series analyses, Poisson regression
model was largely used to examine and forecast the impact of climate variables on
DF. Most studies assessed the impact of temperature fluctuations, precipitation and
relative humidity on DF. Many of them reported positive association between
temperature variation and DF incidence (Pinto et al., 2011, Descloux et al., 2012,
Bannister-Tyrrell et al., 2013, Chen and Hsieh, 2012, Chen et al., 2010, Earnest et
al., 2012). One study examined the effects of sunshine duration on DF incidence and
reported that the risk of DF inversely associated with duration of sunshine, the
number of DF cases decreased as the sunshine increased (Pham et al., 2011). The
optimum time for forecast DF outbreak depending on climatic condition was
estimated between 1 to 5 months (Chen et al., 2010, Hii et al., 2012a, Hii et al.,
2012b). Two studies included both climatic and social factors when examining the
impact of climate variation on DF (Oki and Yamamoto, 2012). According to Oki and
Yamamoto 2012, the monthly mean temperature has increased in Singapore by 0.50C
in the past 30 years due to climate change, but the impact of this temperature change
on DF was minor comparative to the impact of fluctuations in population immunity
and hyperendemicity (Oki and Yamamoto, 2012, Sriprom et al., 2010). The original
Chapter 2: Literature Review 43
systematic review showed that similar kind of study was absent in some endemic
countries like Bangladesh, Sri Lanka and Myanmar. However, in the updated
literature review, we identified a total of seven studies which conducted in those
countries over the last three years, two studies from Bangladesh (Karim et al., 2012,
Hashizume et al., 2012), one from Malaysia (Rohani et al., 2011), one from New
Caledonia (Descloux et al., 2012), two from Vietnam (Pham et al., 2011, Cuong et
al., 2011) and one from Myanmar (Oo et al., 2011).
2.11.2 Association between socio-environmental factors and DF
Among 24 articles, four studies were found which examined the impact of
different social and environmental factors on DF transmission in the Asia-Pacific
region during 2010 - 2013. A study in Vietnam explored that the risk of developing
symptomatic DF for both primary and secondary infection increased with patient’s
age. Therefore, older age group like adolescent and adults are more likely to develop
symptomatic DF than children (Thai et al., 2011). However, the risk of severe
complication including death is higher in children than adults (Anders et al., 2011).
The results of this study support the notion that the people of the South east Asia
would be at higher risk of DF due to the ageing population (Cummings et al., 2009).
Ethnicity and co-morbidity were also identified as important risk factors for DF. A
study in Singapore showed that the likelihood of Chinese patient developing DHF is
1.9 times higher than Indian or Malay (Pang et al., 2012). In addition, they found that
individuals with diabetes mellitus and hypertension have greater risk of developing
DHF compared with individuals with no diabetes mellitus and no hypertension.
Schmidt et al. (2011) have explored the role of population density and socio-
economic factors (water supply infrastructure) as risk factors for DF (Schmidt et al.,
2011). They showed that DF transmission may occur in remarkably low population
density with high vector-host ratio in absence of tap water supply. Lack of reliable
water supply in the immediate vicinity of a household requires constant storing of
water which provides breeding sites for Aedes mosquitoes and increases the risk of
DF transmission.
44 Chapter 2: Literature Review
2.12 KNOWLEGE GAPS
There are a number of knowledge gaps according to the current literature
including:
• The spatiotemporal trends of DF remain unclear, although DF is the
most common arboviral disease in the Asia-Pacific region.
• Literature indicates that global climate change is likely to affect the
seasonal and geographical distribution of DF in the Asia-Pacific region.
However, empirical evidence linking DF to climate change is
inconsistent across geographical locations.
• Region-specific predictive models under different climate change
scenarios are yet to be developed for the surveillance and control of DF.
• The impact of socio-environmental changes on DF transmission is less
studied. Hence, relative importance and interaction between socio-
environmental and climatic factors remain to be elucidated and need
further exploration.
Chapter 3: Results Paper 1 45
Chapter 3: Results Paper 1
Dynamic spatiotemporal trends of dengue transmission in the Asia-Pacific region during 1955 -
2004
Provisionally accepted by PLoS ONE.
Institute for Scientific Information (ISI) Impact Factor: 3.7
Authors: Shahera Banu1, Wenbiao Hu1, Yuming Guo2, Suchithra Naish1 and
Shilu Tong1
Affiliations:
1. School of Public Health and Social Work, Queensland University of Technology,
Brisbane, Australia.
2. School of Population Health, University of Queensland, Brisbane, Australia.
Authors Contribution:
Shahera Banu performed all data analyses and wrote the manuscript. Shilu
Tong and Wenbiao Hu supervised the study and assisted with writing the manuscript.
Yuming Guo helped with software and Suchithra Naish assisted with data analysis
and provided feedback on initial drafts of manuscript.
46 Chapter 3: Results Paper 1
3.1 ABSTRACT
Background: Dengue fever (DF) is one of the most important emerging
arboviral diseases in humans. Globally, DF incidence has increased by 30-fold over
the last fifty years and the geographic range of the virus and its vectors has expanded
into new areas. The disease is now endemic in more than 120 countries in the
tropical and subtropical parts of the world. We examined the spatiotemporal trends of
DF transmission in the Asia-Pacific region during a 50-year study period and
identified the high risk cluster areas.
Methodology and Findings: We obtained annual numbers of DF cases for 16
countries of the Asia-Pacific region from the World Health Organization DengueNet
for the period 1955 to 2004. The fifty-year data were divided into five time periods,
with ten years in each time period, to investigate the trends of DF transmission.
Space-time cluster analyses were conducted using the scan statistics to detect the
high risk disease clusters. We observed an increasing trend in the spatiotemporal
distribution of DF in the Asia-Pacific region over the study period. Thailand,
Vietnam, Laos, Singapore and Malaysia were identified as the highest risk areas
(Relative risk =13.02) for DF transmission in this region during 1995 to 2004. This
study also indicates that the geographic expansion of DF transmission in the Asia-
Pacific region mostly occurred southward.
Conclusions: This study found that the spatiotemporal distribution of DF in
the Asia-Pacific region has increased in recent years. Thailand, Vietnam, Laos,
Singapore and Malaysia were identified as the highest risk areas for DF in this
region. This information may help to improve DF prevention and control strategies in
the Asia-Pacific region by prioritizing control efforts, where they are most needed.
Chapter 3: Results Paper 1 47
3.2 INTRODUCTION
Dengue fever (DF) is one of the most important emerging arboviral diseases in
humans and is widespread and common in tropical and subtropical parts of the
world. It is estimated that about 3.6 billion of the global population and
approximately 120 million travellers are at risk of DF. The number of annual DF
cases is about 50-100 million and the mortality is approximately 2.5% (Halstead,
2007, Beatty et al., 2011, WHO, 2012a). DF incidence has increased 30-fold over the
last fifty years and the geographic range of the virus and its vectors has expanded
into new areas (Wilder-Smith, 2012). Only nine countries experienced DF epidemics
before 1970, but the disease is now endemic in more than 120 countries in Africa,
America, Eastern Mediterranean, South-east Asia and the Western Pacific (WHO,
2012a). Between 2000 and 2007 at least eight previously DF free countries became
infected. Outbreaks of suspected DF were recorded in Pakistan, Saudi Arabia,
Yemen, Sudan and Madagascar during 2005 and 2006 (Guzman and Istúriz, 2010).
In Asia, epidemic DF was common during the first half of the 20th century
(Gubler, 2004a). Severe DF epidemics first occurred in the Philippines and Thailand
during the 1950s. The recent geographic distribution of DF showed that the disease
has now spread out from Southeast Asian countries west to India, Sri Lanka,
Maldives and east to China. Several Pacific island nations such as the Cook Islands,
Tahiti, New Caledonia, Vanuatu, Niue, and Palau have also experienced DF
outbreaks (Gubler, 1998). Nearly 1.8 billion people living in the Asia-Pacific region
are currently at risk, which accounts for 70% of the global DF risk (WHO, 2012a).
The tropical climate of Asia and the Pacific is suitable for DF transmission. The
presence of four DF serotypes and high population density make this region
permissive for DF activity (Simmons et al., 2012).
48 Chapter 3: Results Paper 1
Geographic information systems (GIS) have widely been used in vector borne
disease epidemiology. GIS used for disease mapping can visualize the spatiotemporal
pattern and variation in disease risk. Monitoring the spatiotemporal trends in disease
occurrence can highlight the changing patterns in risk and help to identify risk factors
(Robertson and Nelson, 2010). Spatial scan statistic is one of the most commonly
used approaches in spatial disease surveillance to explore high risk areas or disease
clusters (Abrams and Kleinman, 2007, Kulldorf, 2010). This method scans a larger
encompassing area for possible disease clusters without a priori specification of their
location and size. It identifies approximate location of clusters and performs
significance tests for each cluster (Kulldorff et al., 1997, Kulldorf, 2010). The scan
statistic has been widely used because of its following features (Kulldorff et al.,
1997, Ngui et al., 2013) : firstly, it adjusts for both inhomogeneous population
density and different confounding factors; secondly, searching for clusters without
specifying their size and location ameliorates the problem of pre-selection bias;
thirdly, the likelihood ratio-based test statistics takes multiple testing into account
and gives a single p value for the test of the null hypothesis; and finally, if the null
hypothesis is rejected, it is possible to specify the approximate location of the cluster
that caused the rejection (Kulldorf, 2010). GIS and spatial analyses were used to
identify geographic patterns and risk factors of DF transmission in different areas
(Thai et al., 2010, Tran et al., 2004, Wen et al., 2006, Wu et al., 2009, Mammen et
al., 2008, Hu et al., 2012). However, the spatial pattern of DF remains unexplored at
a continental level around the world. This study examined the spatiotemporal
patterns of DF in the Asia-Pacific region during 1955 to 2004 and identified DF
clusters I different periods of time. Such information is essential for improving DF
prevention and control strategies.
3.3 METHODS
3.3.1 Study area
The continents of Australasia were selected as the study area as the Asia and
Pacific regions are the most seriously affected by DF. Asia is the largest and most
populous continent in the world with approximately 3.9 billion people. The continent
is located in the eastern and northern hemispheres covering 44,579,000 km2 of the
Earth’s surface. Asia’s climate is moist across southeast region and dry across much
Chapter 3: Results Paper 1 49
of the interior. The monsoon circulation dominates across the southern and eastern
regions, due to the presence of the Himalayas, forcing the formation of a thermal low
which draws in moisture during the summer. South-western parts of the continent are
hot.
The continent of Oceania includes Australia, New Zealand and a number of
widely scattered island nations across the Pacific Ocean. The total land area is
8,536,716 km2 with a population of 37 million. The climate of Oceania's islands is
tropical or subtropical, and ranges from humid to seasonally dry.
3.3.2 Data collection
We obtained the annual number of DF cases at a country level for 16 countries
of the Asia-Pacific region from the DengueNet data query managed by the World
Health Organization (WHO) (WHO, 2012b). DengueNet is an internet based
surveillance tool, established in 2005 to collect and provide current epidemiological
data and trends of DF globally. Currently DF statistics from 1955 onwards are
available in DengueNet. However, many countries did not report their DF outbreak
to WHO during the period of 2005 to 2012. Therefore, we restricted our study to the
period of 1955 to 2004. Among 82 countries of the Asia Pacific region, 22 countries
reported DF outbreaks to the WHO. Among these countries, we included 16
countries in our analyses, as other countries did not report their DF outbreak to WHO
for more than five years. To check the data consistency, we compared the retrieved
dataset for each country with historic DF data published in the literature. Location
information including coordinates, area and population size were collected from the
Central Intelligence Agency (CIA) World Factbook (CIA, 2012). The number of
population censuses varies with country. Therefore, we chose two periods of census,
one closest to the beginning of our study period and another to the end of our study
period. For times before the first census, the population size was set equal to the
population size at the first census time, while for times after the last census time, the
population was set equal to the population at the last census time. Then, linear
interpolation was used to estimate the population for times between censuses.
50 Chapter 3: Results Paper 1
3.3.3 Data Analyses
To investigate the spatial and temporal patterns of DF transmission, the fifty
years data were divided into five time periods with ten years in each period, A: 1955-
1964, B: 1965-1974, C: 1975-1984, D: 1985-1994 and E: 1995-2004. Cumulative
incidence rates for each time period were mapped to visualize the temporal trends of
DF. To calculate the cumulative incidence for each country, we first calculated the
annual DF incidence. The annual DF incidence was calculated by dividing the
number of annual DF cases by the corresponding population and multiplied by
100,000. Then the annual DF incidences were aggregated for each ten year period to
estimate the cumulative incidence.
A disease cluster is an unusually high concentration of disease occurrence in a
region unlikely to occur by chance. To test for the presence of DF clusters, we used
the Kulldorff’s space-time scan statistic (SaTScan) (Kulldorf, 2010). In the analyses,
we assumed that the number of DF cases in each country to be Poisson distributed.
We then tested the null hypothesis that the number of DF cases is randomly
distributed in geographic space and time and the expected DF cases in each area are
proportional to its population (Kulldorf, 2010, Kulldorff et al., 1997). The space-time
scan statistic is defined by a cylindrical window with a circular geographic base and
with height corresponding to time. The cylindrical window is then moved in space
and time, so that we obtained an infinite number of overlapping cylinders of different
sizes and shapes, jointly covering the entire study region, where each cylinder
reflects a possible cluster (Kulldorf, 2010, Kulldorff et al., 1997). For each
cylindrical window, the scan statistic tests the null hypothesis against the alternative
hypothesis that there is an elevated risk of DF within window, compared to outside
window (Kulldorf, 2010). SaTScan detects potential clusters by calculating a
maximum likelihood ratio for each cylindrical window (Kulldorf, 2010). The
window with the maximum likelihood ratio is considered as the most likely cluster.
SaTScan also detects secondary clusters that have a significantly large likelihood
ratio but are not the most likely cluster. To evaluate the statistical significance of
both most likely and secondary clusters, SaTScan generates a large number of
random replication of the dataset under the null hypothesis to obtain the p-value
through Monte Carlo hypothesis testing. Then it compares the rank of the maximum
Chapter 3: Results Paper 1 51
likelihood from the real dataset with the maximum likelihood from the random
dataset (Kulldorff et al., 1997). In our analyses, we used 9999 Monte Carlo
replications.
For cluster specification in space-time analyses, two parameters were set for
the maximum cluster size: the proportion of the population at risk and the proportion
of the study period. The population density in our study area (16 countries) varies
greatly and in disease surveillance, higher numbers of cases are usually expected in
urban areas compared to a similar sized area in the countryside due to the higher
population density. To adjust for this uneven population density and in line with
previous literature on mosquito-borne diseases, we decided to limit our spatial cluster
size to 15% of the population at risk (Naish et al., 2011, Chen et al., 2008). However,
analyses were conducted with maximum spatial cluster sizes of 50%, 40%, 30% and
20% of the population at risk to avoid the pre-selection bias, and the results were
very similar to the 15% population limit. We used a maximum of 50% of the study
period as a maximum cluster size in the temporal window.
We used the SaTScan software (version 9.1.1) for the space-time scan statistic
test (Kulldorf, 2010) and R software (version 2.12.0; R development Core Team
2009) to map all results. The “maptools” package of R was used to translate the
space-time outputs into maps and visualize the DF clusters
Chapter 3: Results Paper 1 52
Table 3.1 Summary statistics for annual number of DF cases in the Asia-Pacific countries during 1955-2004
Countries (N=16) Minimum 25% Median 75% Maximum Mean Std. Deviation
Australia 0 0 0 44 868 88 189
Bangladesh 0 0 0 0 6,104 378 1,300
Cook Islands 0 0 0 25 2,256 126 437
India 0 0 0 773 16,517 1,552 3,609
Indonesia 0 0 6,449 21,552 78,690 14,948 19,258
Laos 0 0 0 1,733 17,690 1,553 3315
Malaysia 0 0 810 5,508 33,895 4,932 9,002
Maldives 0 0 0 0 2,054 99 388
Micronesia 0 0 0 0 700 30 134
Myanmar 0 0 1,795 4,854 16,047 3,177 4,053
Philippines 0 388 1,042 6,342 35,648 4,985 8,236
Singapore 0 91 273 1,268 9,459 1,105 1,797
Sri Lanka 0 0 1 679 15,408 942 2,621
Thailand 0 5,914 23,018 45,555 1,74,285 33,814 38,637
Tuvalu 0 0 0 0 811 17 114
Vietnam 0 40 27,306 49,668 3,54,517 41,819 63,532
Chapter 3: Results Paper 1 53
Figure 3.1 Total numbers of countries with DF outbreaks in the Asia-Pacific region, during 1955-2004. (Data source: WHO DengueNet)
Chapter 3: Results Paper 1 55
Figure 3.2 Cumulative incidence of DF in the Asia-Pacific countries. A: 1955-1964, B: 1965-1974, C: 1975-19854,D:1985-1994,E:1995-2004.The X and Y axis of the map show the longitude and latitude respectively.
56 Chapter 3: Results Paper 1
3.4 RESULTS
3.4.1 Descriptive statistics
The annual number of DF for the selected countries ranged from zero to
3,54,517 cases during 1995-2004 (Table 3.1). When present, the lowest average
number of DF cases was reported in Tuvalu (17) and the highest was in Vietnam
(41,819). We observed that the number of countries with DF dramatically increased
over time (Figure 3.1). 22 (26%) countries of the Asia-Pacific region reported at least
one DF outbreak in the fifty years from 1955 to 2004.
3.4.2 Trends of DF transmission
Figure 3.2 shows that the DF endemic areas had expanded geographically in
the Asia-Pacific region over the 50-year study period. More and more countries were
affected by DF over time. On average at least two new countries experienced DF
outbreaks in each decade (Figure 3.2). Thailand, Vietnam, Singapore and Philippines
were affected by DF in the earliest years during 1955-1964 which suggest that any of
these countries can be the origin of DF transmission in this region. Figure 3.2 also
shows that the geographic expansion of DF mainly occurred southwardly in the Asia-
Pacific region. The countries in the south of Thailand or Philippines like Indonesia,
Malaysia, Australia and other Pacific Islands became infected by DF in recent years.
The highest DF incidence was observed in the Cook Islands (2,123/100,000 people)
during 1995 to 2004.
Chapter 3: Results Paper 1 57
Table 3.2 Space-time clusters of DF transmission in the Asia-Pacific region during 1955- 2004
No.Obs, number of observed cases; No. Exp, number of expected cases; LLR, Log -likelihood Ratio.٭P<0.05; †Most likely cluster
Cluster Countries Radius (km) Time frame No. Obs. No. Exp. Relative risk LLR٭
1955-1964 1† Thailand 0 1960/1/1 to 1964/12/31 18337 527.21 96.13 54814.34 2 Philippines 0 1960/1/1 to 1964/12/31 3092 571.13 5.95 2819.03
1965-1974
1† Singapore, Malaysia, Thailand, Vietnam 1711.16 1971/1/1 to 1974/12/31 76393 6143.11 24.05 142940.013
2 Philippines 0 1966/1/1 to 1966/12/31 9384 528.09 18.88 18411.19 3 Myanmar 847.81 1974/1/1 to 1974/12/31 2477 1755.02 1.42 133.25
1975-1984 1† Vietnam, Laos, Thailand 812.13 1980/1/1 to 1984/12/31 510942 38105.18 31.06 1024627.77 2 Cook Islands 0 1980/1/1 to 1980/12/31 357 1.31 273.01 1646.82 3 Malaysia 0 1982/1/1 to 1982/12/31 3052 1114.03 2.75 1140.04 4 Indonesia 0 1983/1/1 to 1984/12/31 26585 23294.07 1.15 228.63
1985-1994 1† Vietnam, Laos, Thailand 812.13 1987/1/1 to 1991/12/31 1034416 79277 27.18 2009818.49 2 Cook Islands 0 1991/1/1 to 1991/12/31 1776 2.54 699.17 9858.32 3 Indonesia 0 1988/1/1 to 1988/12/31 44573 23048 1.96 7995.79 4 Maldives 0 1988/1/1 to 1988/12/31 2054 30.41 67.61 6630.25 5 Philippines 0 1991/1/1 to 1991/12/31 11317 8290 1.37 497.68
1995-2004
1† Singapore, Malaysia,
Thailand, Vietnam, Laos
1872.04 1995/1/1 to 1998/12/31 852301 95356.45 13.02 1243215.57
2 Philippines 0 2001/1/1 to 2004/12/31 94651 45564.40 2.12 21013.62 3 Sri Lanka, Maldives 983.29 2002/1/1 to 2004/12/31 29895 8771.22 3.44 15623.32
58 Chapter 3: Results Paper 1
Figure 3.3 Space-time clusters of DF transmission in the Asia-Pacific region. A: 1955-1964, B: 1965-1974,C:1975-19854,D:1985-1994,E:1995-2004. The X and Y axis of the map show the longitude and latitude respectively.
Chapter 3: Results Paper 1 59
3.4.3 Space-time clusters
Table 3.2 shows the results of the space-time cluster analysis, stratified in five
time periods. Using a maximum cluster size of 15% of the population at risk,
Thailand (RR=96.13) was identified as the most likely cluster by SaTScan and the
Philippines (RR=5.96) was the secondary cluster during 1955-1964. The most likely
cluster covered four countries (Singapore, Malaysia, Thailand and Vietnam) with a
radius of 1,711.16 km detected in 1965-1974. Thus, the DF cluster areas
substantially increased during 1965-1974 compared to the previous ten years and this
trend continued in the following years. In the recent decade (1995-2004), eight
countries were identified as a statistically significant cluster for DF. The most likely
cluster includes Singapore, Malaysia, Thailand, Vietnam and Laos (radius =1,872.04
km, RR=13.02). Overall, we observed that the DF cluster areas in the Asia-Pacific
region had expanded over time (Figure 3.3).
3.5 DISCUSSION
The results of this study indicate that the geographical range of DF
transmission in the Asia-Pacific region had expanded during 1955 to 2004. On
average, at least two countries entered into the DF cluster areas every ten years.
There are many factors which can be responsible for this geographic expansion of
DF in this region during the 20th century: for example, unprecedented population
growth, unplanned urbanization, lack of effective vector control and international
travel (Tatem et al., 2006, Gubler, 2011). The movement of troops and war materials
during World War II may have played a crucial role in the dissemination of Aedes
mosquitoes and the virus (Gubler, 2002a, Gubler, 2011). Another important factor
can be the major economic growth in Southeast Asia, which occurred after World
War II (Gubler, 2004a, Kyle and Harris, 2008). This economic growth leads to
unplanned urbanization where millions of people live in shanty towns with
inadequate housing, lack of water supplies and waste management facilities. This
overcrowded community with a large mosquito population creates ideal conditions
for DF transmission (Arunachalam et al., 2010, Gubler, 2004b, Schmidt et al., 2011).
A recent study in Taiwan showed that urbanization and increased temperature due to
climate change are the most important risk factors for DF transmission (Wu et al.,
60 Chapter 3: Results Paper 1
2009). In addition, increased use of modern transportation due to globalization is
responsible for the importation of the dengue virus through viremic individuals and
dispersal of exotic mosquitoes into new areas. It has been suggested that Aedes
albopictus was introduced into many Pacific islands through modern container ships
(Tatem et al., 2006, Wilder-Smith and Gubler, 2008).
Global climate change has also been suggested to be an important factor for the
range expansion of DF in Asia (Benitez, 2009). Climatic factors, including
temperature, rainfall and humidity, have direct and indirect impacts on mosquito
survival, their life span and reproductive rate, which can influence the geographic
distribution of vectors and the virus (Patz et al., 2005). An association between DF
incidence and rainfall was reported in many countries of the Asia-Pacific region and
the outbreaks usually coincided with the rainy season (Banu et al., 2011). Rainfall
can potentially increase the number of mosquito breeding sites and thus can increase
the chance of DF transmission (Arcari et al., 2007).
Thailand, Vietnam, Laos, Singapore and Malaysia were identified as the most
likely cluster in the most recent years (1995 - 2004). DF transmission in this area
follows a cyclical pattern with highest incidence in the hot and rainy seasons from
May to October (Barbazan et al., 2002, Cuong et al., 2011). We know that DF
infection in travellers depends on destination, season of travel, duration of stay and
epidemic activity. Therefore, travellers to these countries may need to take
precaution like avoiding the monsoon season and shortening the duration of their
stay if a DF outbreak occurs. This awareness may significantly reduce the risk of DF
transmission to the non-endemic areas.
Our results also suggest that the geographic expansion of DF in the Asia-
Pacific region might originate from the Philippines or Thailand. These two countries
were identified as DF clusters as early as 1960. Many other studies also suggested
that Philippine or Thailand can be the origin of DF transmission in Asia (Gubler,
1998). Historically, the first severe DF outbreak occurred in Manila and Philippines
Chapter 3: Results Paper 1 61
in 1953 followed by Bangkok and Thailand (Gubler, 2011, Guzman et al., 2010).
The geographic expansion of DF in the Asia-pacific region mostly occurred
southwardly. Global climate change might explain this southward expansion of DF
to some extent. In the past 100 years, mean surface temperature has increased by 0.3-
0.8oC across the continent, (IPCC, 2007) which could create climatic conditions
suitable for dengue mosquito vector and facilitated DF transmission in this region
(Benitez, 2009, Kyle and Harris, 2008, Hales et al., 2002). A southward expansion in
DF transmission was also observed in Argentina and Australia (Woodruff et al.,
2006, Hu et al., 2011, Carbajo et al., 2012). However, it is still debated whether the
southward expansion is real or an artefact (Russell, 2009).
This study has several strengths. This is the first empirical study exploring the
spatiotemporal pattern of DF transmission in the Asia-pacific region. DF data from
16 countries for 50 years were used in this study. It indicates the necessity for future
research assessing important determinants of DF emergence and rapid geographic
expansion in this region. It also suggests the importance of exploring the DF
transmission pattern within countries.
The main limitations of this study include the low resolutions of the DF
dataset. We only obtained annual country level data for DF. It would be better if we
could have higher resolution such as monthly or weekly data in this study which
would also show the seasonal variation in DF transmission, but this information is
not available for most countries in the WHO DengueNet. Additionally, we did not
include some DF endemic countries in the Asia-Pacific region like Taiwan and China
in this study due to the unavailability of data. Inclusion of these data might increase
the DF cluster area and help us to justify the southward expansion of DF in this
region. There are also some issues in the quality of the WHO DengueNet data. Under
reporting is possible when some countries did not report DF outbreak information to
the DengueNet for years which might bias our results. Over reporting is also possible
as some countries use only clinical diagnosis rather than serological diagnosis which
cannot differentiate DF from other diseases like chikungunya.
62 Chapter 3: Results Paper 1
In summary, this study found that the spatial and temporal distribution of DF in
the Asia-Pacific region increased over the 50-year study period. Social, ecological
and demographic changes that have occurred in recent years are thought to be
responsible for the geographic expansion of DF. Global climate change can further
expand the geographic limit of DF. Thailand, Vietnam, Laos, Singapore and
Malaysia were identified as the most likely cluster for DF in this region. This
information may help to improve DF prevention and control strategies in the Asia-
Pacific region by prioritizing control efforts, where they are most needed.
Financial support
The work was supported by QUT postgraduate scholarships (SB), NMHRC
postdoctoral training fellowship (WH) and a NMHRC research fellowship (ST). The
funders had no role in study design, data collection and analysis, decision to publish,
or preparation of the manuscript.
Conflict of interest
We declare that we have no conflict of interest.
Chapter 3: Results Paper 1 63
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Chapter 4: Results Paper 2 67
Chapter 4: Results Paper 2
Space-time clusters of dengue fever in Bangladesh
Published in Tropical Medicine and International Health
Institute for Scientific Information (ISI) Impact Factor: 2.8
Citation:
BANU, S., HU, W., HURST, C., GUO, Y., ISLAM, M. Z. & TONG, S. 2012. Space-
time clusters of dengue fever in Bangladesh.Tropical Medicine and International Health, 17,
1086-1091.
Authors Contribution:
Shahera Banu performed all data analyses and wrote the manuscript. Shilu Tong and
Wenbiao Hu supervised the study and assisted with writing the manuscript. Cameron Hurst
contributed to the manuscript in terms of providing feedback on initial drafts. Yuming Guo
helped with software and Mohammad Zahirul Islam provided boundary dataset for mapping.
68 Chapter 4: Results Paper 2
4.1 SUMMARY
OBJECTIVE: To examine the space-time clustering of dengue fever (DF) transmission
in Bangladesh using geographical information system and spatial scan statistics (SaTScan).
METHODS: We obtained data on monthly suspected DF cases and deaths by district in
Bangladesh for the period of 2000-2009 from the Directorate General of Health Services.
Population and district boundary data of each district were collected from national census
managed by Bangladesh Bureau of Statistics. To identify the space-time clusters of DF
transmission a discrete Poisson model was performed using SaTScan software.
RESULTS: The results indicate that space-time distribution of DF transmission was
clustered during three different periods 2000-2002, 2003-2005 and 2006-2009. Dhaka was
identified as the most likely cluster for DF in all three periods. Several other districts were
identified as significant secondary clusters. However, the geographic range of DF
transmission appears to have declined in Bangladesh over the last decade.
CONCLUSION: There were significant space-time clusters of DF in Bangladesh over
the last decade. Our results would prompt future studies to explore how social and ecological
factors may affect DF transmission and would also be useful for improving DF control and
prevention programs in Bangladesh.
Chapter 4: Results Paper 2 69
4.2 INTRODUCTION
Dengue fever (DF) is one of the most important emerging arboviral diseases worldwide
(WHO, 2000). The virus is transmitted through the bite of container breeding mosquitoes
Aedes aegypti and Aedes albopictus which are present in most tropical and subtropical
countries (Rigau-Perez et al., 1998). Symptoms of DF infections in human vary from mild flu
like DF to life threatening dengue hemorrhagic fever (DHF) or dengue shock syndrome
(DSS)(Gubler, 2002a). It has been estimated that about 50 million people worldwide become
infected with the dengue viruses annually. Globally, the geographic range of DF transmission
has increased dramatically in recent years (WHO, 2000). It has been hypothesized that many
social and demographic changes such as population growth, urbanization, air travel and
climate change contribute to the increased incidence and geographical expansion of DF
transmission (Gubler, 2002a, Wu et al., 2007).
DF has become a serious public health concern in Bangladesh after the first large scale
outbreak occurred in 2000. However, evidence suggests that sporadic DF outbreaks occurred
in Bangladesh between 1964 and 1999 (Hossain et al., 2003, Rahman et al., 2002). Since
2000, DF cases have been reported every year in all major cities of Bangladesh. More than
23,872 cases were reported to the Directorate General of Health Services (DGHS) and 233
were fatal between 2000 and 2009. The worst outbreak was in 2002 with 6,132 cases and 58
deaths. Both Aedes aegypti and Aedes albopictus were identified as potential vectors for DF
transmission in Bangladesh (Ali et al., 2003). A national guideline based on the WHO
protocol was developed by DGHS in 2000 to control DF transmission and reduce its
morbidity and mortality (DGHS, 2000).
In the absence of effective vaccine and specific treatment, vector control is the only
way to prevent DF transmission. Previous studies suggest that the risk of DF transmission
varies over space and time (Mammen et al., 2008, Tran et al., 2004, Thai et al., 2010).
Therefore, an identification of high risk areas can be useful for prioritizing DF surveillance
and vector control efforts in areas where they are most needed (Ali et al., 2003). In this study,
we investigated the spatial and temporal distribution of DF at a district level in Bangladesh
during 2000 - 2009. Our aim was to identify high risk clustering areas for DF transmission in
Bangladesh using space-time scan statistics and geographic information system.
70 Chapter 4: Results Paper 2
4.3 METHODS
4.3.1 Study area
Bangladesh is located in South Asia between latitude of 20-27oN and longitude of 88-
93oE. It is a low-lying, riverine country with a large marshy jungle coastline of 710 km on the
northern littoral of the Bay of Bengal. It is one of the most densely populated countries
(density 964/km2) in the world and covers 147,570 km2. Bangladesh is divided into seven
administrative divisions and these are subdivided into districts. There are 64 districts in
Bangladesh, each further subdivided into Upazila or Thana. Dhaka is the capital and largest
city of Bangladesh. Other major cities include Chittagong, Khulna, Rajshahi, Sylhet and
Barisal. Bangladesh has a tropical monsoon climate characterized by wide seasonal variation
in rainfall, temperatures and humidity. Regional climatic differences in this flat country are
minor. Four seasons are generally recognized; hot (21.7 - 35.5 0C), muggy summer from June
to August; humid and rainy autumn from September to November; cool (11.7 - 26.8 0C) and
dry winter from December to February; and warm spring from March to May. About 80 % of
Bangladesh's rain falls during the wet monsoon season from June to November.
4.3.2 Data collection
As DF is a notifiable disease in Bangladesh, any DF case detected based on the
“clinical case definitions” of National guidelines for clinical management of dengue
syndrome must be reported to the DGHS through the Civil Surgeon of the district. A DF
suspected case is defined by the presence of acute fever accompanied by any two of the
following clinical symptoms such as headache, myalgia, arthralgia, rash, positive tourniquet
test and leucopenia, absence of any other febrile illness and high index of suspicion based
on period, population and place (DGHS, 2000). We obtained computerized datasets
containing the number of monthly suspected DF cases and deaths by district in Bangladesh
for the period of 1 January 2000 to 31 December 2009 from DGHS. There has been no
significant change in the dengue reporting system since 2000 in Bangladesh. Relevant
population data and electronic boundaries of each district were retrieved from the national
census database managed by Bangladesh Bureau of Statistics (BBS). District information
includes district name, code, area (km2), digital boundaries and base maps. The district
population were generated from the 2001 and 2010 census data and for the remaining years,
the district population were estimated based on linear interpolation (Kulldorf, 2010). This
Chapter 4: Results Paper 2 71
study was approved by the Human Research Ethics Committee, Queensland University of
Technology.
4.3.3 Statistical analysis
A space-time statistical analysis was applied to detect high risk clusters of DF using
SaTScan software (version 9). Case files generated using monthly aggregated numbers of DF
cases for each district was used in data analysis. Population and coordinates data were also
used as inputs in SaTScan. We fit a discrete Poisson regression model to identify space-time
clusters after adjustment for the uneven geographical density of district population (Kulldorf,
2010, Kulldorf et al., 1998).
For cluster specification in space-time analyses, three parameters were set for cluster
size: the maximum circle radius in the spatial window, maximum temporal window and the
proportion of the population at risk. Of the 64 districts 99% of them were within a 40 km
radius. Thus, 40 km was chosen as the maximum circle radius for all analyses. Space-time
analyses were also performed using 10 km, and 20 km of maximum circle radius and the
results obtained were very similar to those using 40 km. The analyses were conducted using a
maximum spatial cluster size of 50% of the population at risk in the spatial window and a
maximum of 50% of the study period in the temporal window. Because of the differences in
population densities across Bangladesh, it was decided to limit the spatial cluster size to 50%
of the population at risk. The spatial clusters were also defined to cover less than 25% and
10% of total population at risk and similar results were obtained to the 50% of the population
limit, which suggests that the maximum cluster size is adequate to be limited by 50% of the
population at risk. The most likely and secondary likely clusters were detected through the
likelihood ratio test. Significance of the clusters was evaluated with Monte Carlo simulation
which was set to 9999. MapInfo Professional (version 10.0) was used to display space-time
clusters.
72 Chapter 4: Results Paper 2
4.4 RESULTS
4.4.1 DF epidemics and outbreaks
The epidemic pattern of DF fluctuated from 2000 to 2009 with major outbreaks in 2000
and 2002 (Figure 4.1). The monthly number of DF cases ranged from 0 to 3,281 (mean =
198, standard deviation = 454) and the highest number of cases was reported in August 2002.
DF outbreaks in Bangladesh after 2002 showed a decreasing trend. Although DF outbreaks
were regular in Bangladesh, a one year inter-epidemic period between outbreaks was
apparent. The monthly number of DF deaths ranged from 0 to 33 and no death was recorded
after 2006. Figure 4.1 also shows a striking variation in monthly numbers of districts with DF
infection from 2000 to 2009. Peaks in DF cases generally coincided with high monthly
numbers of districts with DF.
4.4.2 Disease clusters
The cluster analyses show that the space-time distribution of DF was clustered during
three periods 2000-2002, 2003-2005 and 2006-2009. Using the maximum spatial cluster size
of 50% of the population at risk and a circle radius of 40 km, Dhaka district was identified as
the most likely cluster and Khulna and Chittagong as secondary clusters (Figure 4.2).
Another two districts (Barisal and Jhenaidah) were identified as secondary clusters in 2000
during August to October but those clusters were not found in later years (Figure 4.2). All
clusters were identified between June - November which represents a rainy monsoon season
in Bangladesh.
Chapter 4: Results Paper 2 73
Figure 4.1 Monthly number of DF cases, deaths and districts with DF notification between January 2000 and December 2009 in Bangladesh.
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74 Chapter 4: Results Paper 2
Figure 4.2. Space-time clusters of DF identified in three different periods in Bangladesh.
Chapter 4: Results Paper 2 75
Table 4.1 Space-time clusters of DF in Bangladesh, 2000-2009.
No.Obs, number of observed cases; No. Exp, number of expected cases; LLR, Log -likelihood Ratio.٭P<0.05; †Most likely cluster.
Cluster Name of District
Radius (km)
Area (Sq km)
Time frame No. Obs. No. Exp. Relative risk LLR٭
2000-2002 1† Dhaka
0 307.42 2001/7/1 to 2002/10/31 8037
415.15 43.48 18865.32
2 Khulna 0 56.83
2000/8/1 to 2000/11/30 590 28.39 21.63 1239.71
3 Chittagong 0 773.57
2000/8/1 to 2000/9/30 590 39.40 15.58 1057.01
4 Jhenaidah 0 227.23
2000/8/1 to 2000/8/31 24 4.80 5.00 19.44
5 Barisal 0 515.38
2000/8/1 to 2000/10/31 2.63 21.33 2.63 19.43
2003-2005
1† Dhaka
0 307.42 2004/6/1 to 2005/11/30 4887 181.22 233.31 14773.74
2 Chittagong 0 773.57
2003/10/1 to 2003/11/30 39 15.32 2.56 12.81
2006-2009
1† Dhaka 0 307.42 2006/6/1 to 2006/11/30 2144 35.44 119.80 7326.35
2 Khulna 0 56.83
2006/7/1 to 2006/9/30 46 4.87 9.53 62.32
Chapter 4: Results Paper 2 77
Table 4.1 shows the names of districts included in each cluster, radius (km),
observed cases, expected cases, Relative Risk (RR) and log-likelihood ratio. The
highest risk for the most likely cluster (Dhaka) was found in 2004-2005 during June -
November (RR= 233.3, p< 0.05) and the risk was attenuated in 2006 during June -
November (RR=119.8, p< 0.05) and was lowest in 2000-2002 (RR= 43.48, p< 0.05).
For secondary clusters the RR ranged from 2.56 to 21.63 and the highest risk was
identified for Khulna in 2000 during August - November (RR= 21.64, p< 0.05)
which reduced to 9.53 in 2006 - 2009.
4.4.3 Spatial dispersion of DF
We attempted to identify whether changes in DF transmission varied with
latitude and longitude of district centroids in the periods 2000-2002, 2003-2005 and
2006-2009. A logistic regression model was constructed with the dichotomous
outcome variable defined as whether or not an increase of DF cases occurred in each
district between the three periods. Longitude and latitude of district centroids were
entered as explanatory variables. The results indicate that changes of DF
transmission were not significantly associated with geographic variation (i.e latitude
and longitude) during 2000-2002, 2003-2005 and 2006-2009.
78 Chapter 4: Results Paper 2
4.5 DISCUSSION
The results of this study indicate a significant variation in spatiotemporal
distribution of DF in Bangladesh. The geographic extent of notified DF cases has
declined in Bangladesh over the study period. Dhaka was identified as the highest
risk area for DF transmission in Bangladesh.
Space-time cluster analysis is a valuable tool to examine how spatial patterns
change over time. This study shows that DF transmission in Bangladesh was
clustered in three different periods and provides a clear pattern of DF clustering
within this country. Dhaka was identified as the most likely cluster for DF
transmission. It may be because Dhaka is the largest city in Bangladesh with a high
population density. DF can be easily transmitted by mosquitoes in this area. Several
other districts (Chittagong and Khulna) in the southern part of the country were
identified as secondary clusters (Figure 4.2). These are also the fastest growing cities
located close to seaport and airport. Apart from population density, the movement of
infected individuals and/or mosquitoes into this region from overseas through air
travel or by ship may increase the chance of DF transmission (Sutherst, 2004).
However, rainwater collection for domestic purposes is not a common practice in
Bangladesh, about 36% household of the southwest coastal region of the country use
rainwater due to arsenic contamination in the ground water and high salinity problem
(Ferdausi and Bolkland, 2000). The storage of rainwater in uncovered containers in
this area might increase the risk of DF transmission by providing suitable mosquito
breeding habitats (Beebe et al., 2009). Climatic variation and socio-economic factors
may also play an important role in promoting DF transmission (Hu et al., 2011, Hu et
al., 2012). Further investigation in these high risk areas is required to understand the
dynamics of DF transmission and to discover the role of biological, social and
environmental factors in the transmission of DF.
Chapter 4: Results Paper 2 79
We found that the geographic range of DF clusters has declined in Bangladesh
over the last decade (Figure 4.2). We speculate that the effective management of DF
patients according to the national dengue guidelines and huge public awareness after
the first outbreak in 2000 might have helped to reduce DF transmission in
Bangladesh (Rahman et al., 2002). There is no routine dengue vector control
programme in Bangladesh. Though, irregular mosquito control activities exist in
some cities in Bangladesh which is not specifically for Aedes mosquitoes
(Chepesiuk, 2003). According to Dhaka City Corporation (DCC), they spray
adulticide and larvicide in every summer in the areas with high mosquito population.
DCC also sprays insecticide indoor and outdoor in the notified area when a DF
outbreak occurs. Besides vector control measures, socio-economic changes,
population immunity and changes in viral strain and fitness may also contribute to
the decreased DF incidence in Bangladesh (Cummings et al., 2009, Podder et al.,
2006). Further research is needed to identify possible reasons for declining trend of
DF in Bangladesh.
Dhaka was constantly identified as the most likely cluster and very few
districts were identified as secondary clusters for DF transmission in Bangladesh in
recent years. We believe that patients detected with DF in other districts might have
acquired infections during their visits to Dhaka and symptoms manifested when they
returned to their home districts. Although virus was transmitted to other districts
through infected patients, it might not continue its transmission due to the reduced
number of vectors and the reduced number of virus populations in the dry season
(Aaskov et al., 2006, Williams et al., 2010, Cummings et al., 2004). However, we
were unable to identify the origin of DF transmission in Bangladesh and its spreading
direction due to the lack of information on patient’s location, their movement,
demographics and mosquito control activity. This should be a priority for future DF
research in Bangladesh.
80 Chapter 4: Results Paper 2
This is the first study to examine the spatiotemporal pattern of DF in
Bangladesh. It has clearly demonstrated the heterogeneity of DF risk at the district
level in Bangladesh and revealed the spatiotemporal pattern of DF across the
country. As cluster analysis could be an important tool for decision makers to
prioritize areas where more surveillance and disease prevention efforts are required,
our findings can be useful for the Bangladesh health authority to further improve DF
control and prevention strategies. Additionally, the method developed in this study
may have wider applications in the field of disease surveillance and risk
management.
Our study has three key limitations. Firstly, in our analysis, we used reported
cases aggregated at the district level, which prohibits analysis at a higher spatial
resolution and may lead to important local clusters being missed. Secondly, there is
likely variation in the quality of the DGHS dengue surveillance data. Underreporting
is possible in the DGHS data when people infected by DF had subclinical infection
and did not seek for medical attention. Finally, we only identified potential DF
clusters in this preliminary study, but did not explore possible risk factors associated
with clustering. However, our future research will focus on the investigation of
various climatic, ecological, and socio-demographic determinants of DF in clustered
areas.
4.6 CONCLUSION
In summary, this study demonstrates that DF transmission in Bangladesh was
clustered in different spatial and temporal settings and the geographic distribution of
DF appears to have contracted over recent years. The impact of socio-demographic
changes and climatic factors on DF transmission in clustered areas remains to be
determined. Our findings can be useful for the Bangladesh health authority to further
improve DF control and prevention strategies. Additionally, the cluster methods
developed in this study may have wider applications in the field of disease
surveillance and risk management.
Chapter 4: Results Paper 2 81
Acknowledgments
We thank to Director General of Health Services, Dhaka and Bangladesh
Bureau of Statistics for providing DF case record and census data respectively.
Financial support
The work was supported by QUT postgraduate scholarships (SB, YG),
NMHRC postdoctoral training fellowship (WH) and NMHRC research fellowship
(ST).
Conflict of interest
We declare that we have no conflict of interest.
82 Chapter 4: Results Paper 2
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AASKOV, J., BUZACOTT, K., THU, H. M., LOWRY, K. & HOLMES, E. C. 2006. Long-term transmission of defective RNA viruses in humans and Aedes mosquitoes. Science, 311, 236-238.
ALI, M., WAGATSUMA, Y., EMCH, M. & BREIMAN, R. F. 2003. Use of a geographic information system for defining spatial risk for dengue transmission in Bangladesh: role for Aedes albopictus in an urban outbreak. American Journal of Tropical Medicine and Hygiene, 69, 634-640.
BEEBE, N. W., COOPER, R. D., MOTTRAM, P. & SWEENEY, A. W. 2009. Australia’s dengue risk driven by human adaptation to climate change. PLoS Neglected Tropical Diseases, 3, e429.
CHEPESIUK, R. 2003. Mosquito mismanagement? Environmental Health Perspectives, 111, A636.
CUMMINGS, D. A. T., IAMSIRITHAWORN, S., LESSLER, J. T., MCDERMOTT, A., PRASANTHONG, R., NISALAK, A., JARMAN, R. G., BURKE, D. S. & GIBBONS, R. V. 2009. The impact of the demographic transition on dengue in Thailand: insights from a statistical analysis and mathematical modeling. Plos Medicine, 6, e1000139.
CUMMINGS, D. A. T., IRIZARRY, R. A., HUANG, N. E., ENDY, T. P., NISALAK, A., UNGCHUSAK, K. & BURKE, D. S. 2004. Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature, 427, 344-347.
DGHS. 2000. National guidelines for clinical management of dengue syndrome. Available: http://www.sdnbd.org/sdi/issues/health/dengue/other/dng.PDF [Accessed March 26 2009].
FERDAUSI, S. A. & BOLKLAND, M. W. Rainwater harvesting for application in rural Bangladesh. 26th WEDC conference, water, sanitation and hygiene : challenges of the millennium, 2000 Dhaka, Bangladesh. 16-19.
GUBLER, D. J. 2002. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends in Microbiology, 10, 100-103.
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HOSSAIN, M. A., KHATUN, M., ARJUMAND, F., NISALUK, A. & BREIMAN, R. F. 2003. Serologic evidence of dengue infection before onset of epidemic, Bangladesh. Emerging Infectious Diseases, 9, 1411-1414.
HU, W., CLEMENTS, A., WILLIAMS, G. & TONG, S. 2011. Spatial analysis of notified dengue fever infections. Epidemiology and Infection, 139, 391-399.
HU, W., CLEMENTS, A., WILLIAMS, G., TONG, S. & MENGERSEN, K. 2012. Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia. Environmental Health Perspectives, 120, 260-266.
KULLDORF, M. 2010. SaTScanTM User Guide for Version 9.0. Available: http://www.satscan.org/ [Accessed 20 October 2010].
KULLDORF, M., ATHAS, W., FEUER, E., MILLER, B. & KEY, C. 1998. Evaluating cluster alarms:a space-time scan statistic and brain cancer in Los Alamos, New Mexico. American Journal of Public Health, 88, 1377-1380.
MAMMEN, M. P., PIMGATE, C., KOENRAADT, C. J. M., ROTHMAN, A. L., ALDSTADT, J., NISALAK, A., JARMAN, R. G., JONES, J. W., SRIKIATKHACHORN, A., YPIL-BUTAC, C. A., GETIS, A., THAMMAPALO, S., MORRISON, A. C., LIBRATY, D. H., GREEN, S. & SCOTT, T. W. 2008. Spatial and temporal clustering of dengue virus transmission in Thai villages. Plos Medicine, 5, e205.
PODDER, G., BREIMAN, R. F., AZIM, T., THU, H. M., VELATHANTHIRI, N., KYMLOWRY, L. Q. M. & AASKOV, J. G. 2006. Short report: origin of dengue type-3 viruses associated with the dengue outbreak in Dhaka, Bangladesh,in 2000 and 2001. American Journal of Tropical Medicine and Hygiene, 74, 263-265.
RAHMAN, M., RAHMAN, K., SIDDQUE, A. K., SHOMA, S., KAMAL, A. H., ALI, K. S., NISALUK, A. & BREIMAN, R. F. 2002. First outbreak of dengue hemorrhagic fever, Bangladesh. Emerging Infectious Diseases, 8, 738-740.
RIGAU-PEREZ, J. G., CLARK, G. G., GUBLER, D. J., REITER, P., SANDERS, E. J. & VORNDAM, A. V. 1998. Dengue and dengue haemorrhagic fever. Lancet, 352, 971-977.
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THAI, K. T. D., NAGELKERKE, N., PHUONG, H. L., NGA, T. T. T., GIAO, P. T., HUNG, L. Q., BINH, T. Q., NAM, N. V. & DE VRIES, P. J. 2010. Geographical heterogeneity of dengue transmission in two villages in southern Vietnam. Epidemiology and Infection, 138, 585-591.
TRAN, A., DEPARIS, X., DUSSART, P., MORVAN, J., RABARISON, P., REMY, F., POLIDORI, L. & GARDON, J. 2004. Dengue spatial and temporal patterns, French Guiana, 2001. Emerging Infectious Diseases, 10, 615-621.
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WILLIAMS, C. R., BADER, C. A., KEARNEY, M. R., RITCHIE, S. A. & RUSSELL, R. C. 2010. The extinction of dengue through natural vulnerability of its vectors. PLoS Neglected Tropical Diseases, 4, e922.
WU, P. C., GUO, H. R., LUNG, S. C., LIN, C. Y. & SU, H. J. 2007. Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Tropica, 103, 50-57.
Chapter 5: Results Paper 3 85
Chapter 5: Results Paper 3
Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh.
This paper has been accepted for publication in Environment International.
Institute for Scientific Information (ISI) Impact Factor: 6.2
Authors: Shahera Banu1, Wenbiao Hu2, Yuming Guo2, Cameron Hurst3 and
Shilu Tong1
Affiliations:
1. School of Public Health and Social Work, Queensland University of
Technology, Brisbane, Australia.
2. School of Population Health, University of Queensland, Brisbane, Australia.
3. Clinical Epidemiology Unit, Khon Kaen University, Khon Kaen, Thailand.
Authors Contribution:
Shahera Banu performed all data analyses and wrote the manuscript. Shilu
Tong and Wenbiao Hu supervised the study and assisted with writing the manuscript.
Cameron Hurst contributed to statistical support and Yuming Guo helped with the R
software and related packages.
86 Chapter 5: Results Paper 3
5.1 ABSTRACT
Weather variables, mainly temperature and humidity influence vectors, viruses,
human biology, ecology and consequently the intensity and distribution of the
vector-borne diseases. There is evidence that warmer temperature due to climate
change will influence the dengue fever (DF) transmission. However, long term
scenario-based projections are yet to be developed. Here, we assessed the impact of
weather variability on DF transmission in a megacity of Dhaka, Bangladesh and
projected the future DF risk attributable to climate change. Our results show that
weather variables particularly temperature and humidity were positively associated
with DF transmission. The effects of weather variables were observed at a lag of four
months. We projected that assuming a temperature increase of 3.3oC without any
adaptation measure and changes in socio-economic condition, there will be a
projected increase of 16,030 DF cases in Dhaka by the end of this century. This
information might be helpful for the public health authorities to prepare for the likely
increase of DF due to climate change. The modelling framework used in this study
may be applicable to DF projection in other cities.
Chapter 5: Results Paper 3 87
5.2 INTRODUCTION
According to the Intergovernmental Panel on Climate Change (IPCC), the
global temperature increased significantly over the 20th century (IPCC, 2007). Recent
trends in anthropogenic emissions and their modelled impacts of global climate
strongly suggest that both emissions and warming trends will continue to affect the
atmospheric process in the 21st century. It has been predicted that the global mean
temperature will increase by 1.1-6.4 oC by the end of this century (IPCC, 2007).
Annual average temperature for the South Asia region has been projected to rise by
3.3oC (range, 2 - 4.4 oC) in 2100, with summer temperature increases of 2.7 oC
(IPCC, 2007). A growing body of literature suggests that warmer temperatures will
enhance the transmission rate for mosquito-borne disease and will widen its
geographical distributions (McMichael et al., 2006, Hales et al., 2002, Kan et al.,
2012, Jetten and Focks, 1997).
Dengue fever (DF) is one of the most important mosquito-borne disease of
humans, and has emerged as a global public health concern throughout the tropical
and subtropical regions of the world (Gubler, 1998). DF transmission in these areas
typically follows a seasonal pattern which reflects the influence of weather on the
transmission cycle (Johansson et al., 2009b). DF is weather sensitive due to its
mosquito vector, which requires standing water to breed and warm ambient
temperature for larval development and virus replication (Patz et al., 1998, Hopp and
Foley, 2001, Banu et al., 2011). The incidence of DF has increased significantly in
last 35 years and various factors including urbanization, globalization and climate
change are thought to be the major contributors (Hopp and Foley, 2001, Gubler,
2011).
88 Chapter 5: Results Paper 3
Several recent studies have demonstrated an association between weather
variability and DF (Chen and Hsieh, 2012, Hii et al., 2009, Wu et al., 2007, Hu et al.,
2012). Temperature, rainfall and humidity were found to be associated with DF
transmission (Wu et al., 2007, Bangs et al., 2006, Ram et al., 1998, Karim et al.,
2012). However, the magnitude of the association between weather and DF varied
with geographical location and socio-environmental conditions (Thammapalo et al.,
2007, Arcari et al., 2007). It is also evident that El Niño events have strongly
associated with DF epidemics, although spatial heterogeneity exists in this relation
(Cazelles et al., 2005, Hu et al., 2010). Mathematical modelling has recently been
used to measure and predict the impact of weather variation on DF and significant
advances have been achieved in modelling approaches (McMichael et al., 2006, Hu
et al., 2010). Many studies around the world have developed different models to
predict the future distribution of DF in response to climate change (Hales et al., 2002,
Hopp and Foley, 2003, Patz et al., 1998). Such projections can help to combat the
increased risk of DF due to climate change by taking necessary adaptation measures.
However, very few studies were conducted to identify the association between
weather variables and DF transmission in the South Asian region and long term
scenario-based projections are yet to be developed (Banu et al., 2011, Karim et al.,
2012, Oo et al., 2011, Chakravarti and Kumaria, 2005). In this study, we examined
the effects of weather variability on DF transmission and projected the potential
impact of climate change on the pattern of DF in the megacity of Dhaka.
Chapter 5: Results Paper 3 89
5.3 MATERIALS AND METHODS
5.3.1 Study area
This study carried out in Dhaka, the capital of Bangladesh. Our previous study
showed that Dhaka is the highest risk area for DF transmission in Bangladesh and the
underlying cause of increased risk of DF in this location remains unknown, which
requires further investigation (Banu et al., 2012). Dhaka is located in central
Bangladesh at 23°42′ north latitude and 90°22′ east longitude with an area of
1,464 square kilometres. Dhaka along with its metropolitan area had a population of
11.8 million (2011 census), making it the biggest city in Bangladesh. Dhaka has a
hot, wet and humid tropical climate. The city is within the monsoon climate zone,
with an annual average temperature of 25 °C and monthly means varying between 18
°C in January and 29 °C in August. Nearly 80% of the annual average rainfall of
1,854 millimetres occurs between May and September.
5.3.2 Data collection
Data on the monthly number of notified DF cases in Dhaka city were obtained
from the Directorate General of Health Services (DGHS) from January 2000 to
December 2010. As DF is a notifiable disease in Bangladesh, any case detected
based on the World Health Organization (WHO) clinical criteria must report to the
DGHS by the hospital. According to the WHO clinical criteria, a DF case was
defined by the presence of acute fever accompanied by any two of the following
clinical symptoms such as headache, myalgia, arthralgia, rash, positive tourniquet
test and leucopenia (WHO, 2000). We also obtained monthly weather data on
maximum, mean and minimum temperature, relative humidity and rainfall from
Bangladesh Meteorological Department (Dhaka, Bangladesh) between January 2000
and December 2010. We used monthly total number of DF cases, monthly mean
maximum temperature, mean minimum temperature and total rainfall for data
analyses. Population data were collected from Bangladesh Bureau of Statistics
(BBS).
.
90 Chapter 5: Results Paper 3
5.3.3 Data analysis
We used Spearman’s correlation coefficients to summarize the relationships
between independent variables. A Poisson time series model combined with
distributed lag model (DLM) was used to estimate the effects of weather on DF
transmission. The observed number of DF cases followed a quasi-Poisson
distribution and the model allows for over dispersion.
𝑌𝑡 = Poisson(𝜇𝑡), t=1,.........., n
log(𝜇𝑡) = 𝛼 + �𝛽0�𝑇𝑡,𝑙�𝐿
𝑙=1
+ �𝛽1�𝐻𝑡,𝑙�𝐿
𝑙=1
+ �𝛽2�𝑅𝑡,𝑙�𝐿
𝑙=1
+ log(𝑁𝑡) + 𝑠(𝑡, 𝜆) + Ɛ𝑡
Where t is the month of the observation; Yt is the observed monthly DF counts
in month t; α is the intercept; Tt,l ,Ht,l and Rt,l are the matrices obtained by applying
the DLM to temperature, humidity and rainfall, respectively; l is the lag months; L is
the maximum lag; β0, β1 and β2 are the coefficients for Tt,l ,Ht,l and Rt,l, respectively,
Nt is an offset to control for population using a linear function of time based on the
2001 and 2011 census. The s(t,λ) is the natural cubic spline smoothing function of
time with assigned λ of 2 degrees of freedom per year to control for seasonal pattern.
We used a DLM that modelled the main effects as a linear function and the
delayed effects as a polynomial function. The selection of maximum lag was
conducted using model residual checking and we checked maximum lag up to 6
months. We used second order quadratic polynomial smoothing for the lag. The
mean value of each weather variable was used as a baseline (referring value) to
measure the relative risks. We plotted relative risks against weather variables and
lags to show the entire relationship between weather conditions and DF.
Chapter 5: Results Paper 3 91
The climate and DF relationship were examined using different temperature
measures (maximum, mean and minimum temperature) in the DLM. The deviance
was used to choose the best model. Model including maximum temperature was
associated with lower deviance value (Table 5.3). We also compared the deviance for
the association between each weather variables and DF using DLM. The deviance
was also lower compare to other models when maximum temperature and relative
humidity were included (Table 5.4). The goodness-of-fit was performed to check the
model adequacy using auto-correlation functions of residuals and normality of the
residuals. Figure 5.1 shows that there was no significant auto-correlation between
residuals at different lags in the DLM when maximum temperature and humidity
were used as predictor variables. The scatter plot shows that the residuals in the
model fluctuated randomly around zero with no obvious trend. Thus the goodness-
of-fit analyses show that the model fits the data reasonably well. Therefore, we
selected the model including maximum temperature and relative humidity as the best
model and used it to estimate the effects of weather variation on DF transmission
The constructed model was then validated by dividing the data file into two
data sets. The data between January 2000 and December 2008 were used to develop
a DLM and those between January 2009 and December 2010 were used to validate
the model. The validation indicates that the model had reasonable accuracy as the
observed and predicted values were mostly coincided (Figure 5.3). In addition,
adequacy of the model predicting outbreak (≥ 168) was evaluated by sensitivity
analyses. For sensitivity analyses, the monthly number of DF cases was transformed
into a categorical variable (i.e., outbreak/non-outbreak). Then the sensitivity or true
positivity rate (predicted number of months with DF outbreak/observed number of
months with DF outbreak) and specificity or true negativity rate (predicted number
of months without DF outbreak/observed number of months without DF outbreak) of
the predictive model were calculated.
92 Chapter 5: Results Paper 3
The results of the validated model were then applied to future climate change
situations to generate projections for DF risk in the 2100. We used IPCC regional
climate change projection for South Asia, which results in an increase of 3.3 oC in
annual mean temperature between 1980 to 1999 and 2080 to 2099 (IPCC, 2007). We
assumed that warming will be similar to south Asia in Dhaka. We estimated the
future monthly temperature in Dhaka by combining recorded baseline data with
projection. We added 1, 2 and 3.3 oC to the observed monthly temperature in 2010 to
simulate monthly temperatures in 2100. We assumed that there will be no adaptation
to climate change in Dhaka. We calculated the projected temperature related DF risk
in 2100 after adjusting for the 1.3% increase in population, which is the current
population growth rate in Dhaka (population census 2011).
All statistical tests were two-sided and the p <0.01 were considered statistically
significant. We used R software (version 2.12.0; R development Core Team 2009) to
fit all models, with its “dlnm” package to create the DLM (Gasparrini and
Armstrong, 2011).
Chapter 5: Results Paper 3 93
5.4 RESULTS
There were 25,059 DF cases in the Dhaka during the study period. The average
monthly number of DF cases was 168 with an incidence rate of 1.8 per 100,000
populations. Descriptive statistics for each independent and dependent variable are
shown in Table 5.1. The monthly mean minimum and maximum temperature,
rainfall and relative humidity were 21.9oC, 30.7oC, 180.2 mm and 72.77%,
respectively, between 2000 and 2010 in Dhaka. Spearman correlation coefficients
between climatic variables show that all climatic variables were strongly correlated
to each other except the correlation between maximum temperature and relative
humidity (Table 5.2).
The three dimensional plots show the entire relationship between mean
maximum temperature and relative humidity with DF incidence at different lags
(Figure 5.2). The estimated effects of weather variables on DF incidence were linear
in current months and were nonlinear along lags. Temperature and humidity were
positively associated with DF incidence and the highest effects observed at two
months lag. The sensitivity analyses indicate that the overall model agreement was
89%, sensitivity was 84% and specificity was 91% (Table 5.5).
Table 5.6 reveals the estimated DF cases associated with the variation in
temperature due to climate change by the year 2100. We estimated 377 DF cases
attributable to temperature variation in 2010. Assuming a 1oC temperature increase
in 2100, we projected an increase of 583 DF cases. For a 2 oC increase, we projected
an increase of 2,782 DF cases. If temperature increase by 3.3 o C as the IPCC
projected, the consequence will be devastating, with a projected increase of 16,030
cases by the end of this century.
94 Chapter 5: Results Paper 3
Table 5.1 Descriptive statistics of monthly climatic conditions and DF in Dhaka, Bangladesh, 2000-
2010.
Table 5.2 Spearman’s correlation coefficients between monthly climatic variations in Dhaka,
Bangladesh.
Climatic variables Minimum
Temperature Mean Temperature
Maximum Temperature
Rainfall
Mean Temperature 0.97**
Maximum Temperature 0.72** 0.84**
Rainfall 0.80** 0.73** 0.51**
Humidity 0.62** 0.53** 0.14 0.73**
** P < 0.01
Variables N Mean Standard
Deviation
Minimum Maximum
DF 132 168 394 0 3155
Minimum Temperature (oC) 132 21.9 4.3 11.7 26.8
Mean Temperature (oC) 132 26.3 3.5 16.7 30.7
Maximum Temperature (oC) 132 30.7 2.9 21.7 35.5
Rainfall (mm) 132 180.2 195.1 0 839
Relative Humidity (%) 132 72.8 8 53 85
Chapter 5: Results Paper 3 95
Table 5.3 Deviances for the relationship between DF and different temperature measures by DLM
Temperature measure Deviance for DLM
Maximum Temperature 279275.9
Mean Temperature 303311.6
Minimum Temperature 319501.4
Table 5.4 Deviances for DLM using different covariates
DLM Models Deviance
Temperature and Humidity 279275.9
Humidity and Rainfall 327820.9
Temperature and Rainfall 352719.7
96 Chapter 5: Results Paper 3
Table 5.5: Sensitivity and specificity of DLM for dengue occurrence.
Predicted Observed
Total Outbreak Non-outbreak
Outbreak 27 9 36
Non-outbreak 5 91 96
Total 32 100 132
Sensitivity, 27/32 = 84%; Specificity, 91/100 = 91%; Crude agreement or accuracy, (27+91)/132 =
89%.
Chapter 5: Results Paper 3 97
Figure 5.1 Auto-correlation function partial auto-correlation function and scatter plot of residuals for DLM.
Chapter 5: Results Paper 3 98
Figure 5.2 Association between climatic variables (maximum temperature and relative humidity) and DF at different lags.
Chapter 5: Results Paper 3 99
Figure 5.3 Validated distributed lag model of climate variation in Dhaka, Bangladesh (validation period = Jan 2009 – Dec 2010 i.e., the cross validation period).
100 Chapter 5: Results Paper 3
Table 5.6 Changes in annual DF incidence under different scenarios of temperature increase by 2100 in Dhaka, Bangladesh.
Climate change scenarios Projected annual DF incidence
Changes in annual DF incidence
Baseline 377
10C increase 960 583
2 0C increase 3,159 2,782
3.3 0C increase 16,407 16,030
Chapter 5: Results Paper 3 101
5.5 DISCUSSION
Our results show that the monthly temperature and humidity were significantly
associated with the monthly DF incidence in Dhaka, with highest lag effects of two
months. These results are consistent with findings of other studies and may assist to
forecast DF outbreaks in different regions (Hii et al., 2009, Hsieh and Chen, 2009,
Descloux et al., 2012, Johansson et al., 2009b). Temperature and humidity are the
most important weather factors in the growth and dispersion of mosquito vector and
potential predictors of DF outbreaks (Wu et al., 2007, Chen et al., 2010).
Temperature influences the life cycle of Aedes mosquitoes including growth rate and
larval survival and the length of reproductive cycle (Patz et al., 2005, Hopp and
Foley, 2001). Maximum mosquito survival rate of 88-93% were observed between
temperature ranges of 20-30 oC (Tun-Lin et al., 2000). Temperature also affects the
virus replication, maturation and period of infectivity. Higher temperature decreases
the length of viral incubation within the vector, and thus increases the chance of
mosquitoes to become infective in their life span (Patz et al., 1998, Yang et al.,
2009a, Hopp and Foley, 2001). Adult mosquito survival also depends on humidity
(Patz et al., 1998, Hopp and Foley, 2001). Given the relationship between
temperature and DF, the projected change in temperature due to climate change may
exacerbate disease transmission in Dhaka. According to the IPCC, the annual mean
temperature increase will be 3.3 oC by the end of the 21st century in Dhaka. The
projected warming will occur both in summer and winter (IPCC, 2007). As summer
will be warmer than before, it is likely that warmer condition will enhance disease
transmission and will increase DF incidence. In previous years, there were few
reported DF cases in Dhaka during winter season. If the winter temperature increases
as projected, it may become more favourable for DF transmission and extend the
outbreak season. Hence DF outbreak may become more intense in future, if the
climate change happens.
102 Chapter 5: Results Paper 3
There has been a significant emergence of DF in Dhaka during the last decade;
the reasons for this can be multiple. Both climatic and non-climatic factors like
socio-ecological changes, viral serotypes, herd immunity and mosquito control can
influence the risk of DF transmission (Gubler, 2011). Rapid urbanization around
Dhaka city can deteriorate the environmental condition and increase the DF
incidence through enhancing the mosquito breeding habitats. Increased air travel can
facilitate the introduction of new dengue serotypes from neighbouring endemic
countries and can make this region hyper endemic (presence of all four dengue virus
serotypes), which will obviously increase the likelihood of DF epidemic (Tatem et
al., 2006, Karim et al., 2012). Additionally, effective vector control can reduce the
vector density and can decrease the DF transmission.
Temperature and humidity affect the DF occurrence in several subsequent
months. We found that monthly maximum temperature and relative humidity were
associated with DF transmission through a 4-months lag period (highest effects in
two months) which includes the time of replication and development of mosquito
and the incubation period of the virus (time of replication both in vector and host).
Therefore, observed lag effects were biologically plausible and consistent with the
findings of other studies (Wu et al., 2007, Arcari et al., 2007, Hii et al., 2012a). A
previous study in Dhaka reported the positive association between maximum
temperature, relative humidity and DF which is consistent with our findings (Karim
et al., 2012). They also observed the highest lag effects at two months which is
similar to our observation. An accurate early warning system to predict DF
epidemics and enhance the effectiveness of preventive measures largely relies on the
sufficient lag time. Thus, four months lag time could be sufficient to warn people
about the possible disease outbreak and take necessary measures to prevent the
epidemic.
Chapter 5: Results Paper 3 103
Different emissions scenarios were developed by IPCC which have been
widely used in the analysis of possible climate change impacts and options to
mitigate climate change. Each emission scenario represents different demographic,
social, economic, technological and environmental developments which are driving
forces of greenhouse gas emissions. “The A1 scenario family describes a future
world of rapid economic growth, global population that peaks at mid-century and
declines thereafter, and the rapid introduction of new and more efficient
technologies”(IPCC, 2000). The A1B emissions scenarios is one of the A1 group
scenarios which assumes the balanced use of energy system like fossil fuel and non-
fossil energy system. The B2 scenario families focus on local and regional
environmental protection and social equity where global population will increase
continuously with comparatively lower rate with intermediate level of economic
development and less rapid and more diverse technological developments than A1 or
B1. Climate change projections for all continents and sub continental regions of the
world were provided by IPCC (IPCC, 2007). These projections were generated using
multi model data set (MMD) and three emission scenarios B1, A1B and A2.
However, the results of most projections were presented and discussed by IPCC on
the basis of A1B scenario as the global mean surface temperature responses in the
ensemble mean of the MMD model follows a ratio of 0.69:1:1.7 for B1: A1B:A2
scenarios. The local temperature responses for almost all regions also follow the
same ratio. Similar to the IPCC regional climate projections, we used the MMD-A1B
projection scenario to predict and discuss the future temperature related DF risk in
Dhaka.
To the best of our knowledge, this is the first study to project the impact of
climate change on DF transmission in Dhaka. We showed that DF incidence will
increase by more than 40 times in Dhaka in the year 2100 relative to 2010, if the
ambient temperatures increase by 3.3 oC according to the IPCC regional climate
projection. It will have devastating consequences for the already stretched public
health systems in Dhaka due to the population ageing and increased burden of
disease (including chronic disease, infectious disease and injury). Human adaptation
to climate change may influence the likelihood of DF transmission. People may
adapt to higher temperatures through improved building design with glazed
104 Chapter 5: Results Paper 3
windows, piped water, insect screening and air-conditioning. These facilities may
effectively reduce their contacts with vector mosquitoes and even if infected
mosquitoes gain entry to these buildings, the low ambient temperature and artificially
dry environment may decrease their survival rate and reduce the risk of disease
transmission (Reiter, 2001). On the other hand, water storing for domestic purposes
in summer months or during droughts may provide increase number of breeding sites
for mosquitoes and increase the risk of DF transmission (Beebe et al., 2009).
However, there is no information available on how people will adapt to climate
change in Dhaka. Therefore, in our study, we assumed that there will be little
adaptation to climate change in the study site.
The weaknesses of this study must be acknowledged. This is a large scale,
ecologic assessment of the relationship between climate and the DF transmission at a
city level. For a comprehensive and systematic risk assessment, more detailed risk
assessment at a community and individual level is required. Inclusion of other factors
such as mosquito density, population immunity, viral factors and human behaviours
may improve the model. Due to the lack of seroprevalance and entomological data,
these variables could not be included into our model. Adaptation to climate change
and changes in socio-economic trends might influence the likelihood of disease
occurrence. However, we have not accounted for all possible socio-economic
features and climate adaptation behaviour. Underreporting bias is inevitable in the
surveillance data to some extent as people infected with subclinical DF infection did
not seek for medical advice. This model is only applicable to Dhaka and areas with a
similar socio-ecologic background as local data were used in the construction of this
model.
Chapter 5: Results Paper 3 105
5.6 CONCLUSION
This study shows that maximum temperature and relative humidity were best
predictors among the major determinants of DF transmission in Dhaka for the period
of 2000-2010. Projected climate change will increase mosquito activity and DF
transmission in this area. Assuming a temperature increase of 3.3 oC by 2100 as
projected by IPCC, there would be a substantial increase in DF incidence in Dhaka.
Therefore, public health authorities need to be well prepared for likely increases of
DF transmission in this region.
Acknowledgments
We thank to Director General of Health Services, Dhaka and Bangladesh
Bureau of Statistics for providing DF case record and census data respectively. We
would also like to thank Bangladesh Meteorological Department, Dhaka for
providing climate data. We thank to Associate Professor Adrian Barnett and Dr.
Weiwei Yu for their valuable comments on the manuscript.
Financial support
The work was supported by QUT postgraduate scholarships (SB), NMHRC
postdoctoral training fellowship (WH) and NMHRC research fellowship (ST).
Conflict of interest
We declare that we have no conflict of interest.
106 Chapter 5: Results Paper 3
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110 Chapter 6: General Discussion
Chapter 6: General Discussion
Chapters 3-5 present the main findings, as well as discussion and conclusions
of each study in turn. The connection and major features of three results papers are
shown in Figure 6.1. This Chapter discusses the key findings of this thesis and the
overall strengths and limitations. This chapter also makes recommendations for
future research directions and then draws conclusions.
6.1 SUBSTANTIVE DISCUSSION
This thesis investigated the spatiotemporal pattern of DF in the Asia-Pacific
region and projected the future risk of DF transmission under different climate
change scenarios in Dhaka, Bangladesh. Although a number of previous studies
identified the association between climate variation and DF transmission (Halide and
Ridd, 2008, Thammapalo et al., 2005a, Chen and Hsieh, 2012, Hii et al., 2009, Wu et
al., 2007, Hu et al., 2012), knowledge on long term impacts of climate change on DF
is limited, particularly in the Asia-Pacific region. There is a need to better understand
the spatiotemporal pattern of DF and the potential impact of climate change on DF
transmission in this region.
I examined the dynamic spatiotemporal pattern of DF transmission in the Asia-
Pacific region over the fifty years from 1955 to 2004 using spatial analysis
techniques (Chapter 3). Fifty years data of DF outbreaks for sixteen countries of this
region were used in this study. The results of the spatiotemporal analysis indicate
that there was a remarkable variation in the spatial distribution of DF in the Asia-
Pacific region. The geographic range of DF was expanded over the last 50 years and
the disease was clustered in high risk areas. Socio-ecological changes occurred in
this region during the 20th century are thought to be responsible for this geographic
expansion (Gubler, 2011, Tatem et al., 2006). World War II might have influenced
the DF transmission in this region through ecological disruption and troop’s
Chapter 6: General Discussion 111
movement. The movement of troop and war materials helped the dissemination of
Aedes mosquitoes and the virus between countries (Gubler, 2002a). Besides this,
massive urbanization, rapid economic growth, increased air travel and climate
change may also contribute to the increased DF transmission and its expansion in the
Asia-Pacific region (Arunachalam et al., 2010, Gubler, 2004b, Schmidt et al., 2011,
Wu et al., 2009, Wilder-Smith and Gubler, 2008). Further research is required to
confirm the contribution of these factors to the pattern of DF transmission.
Thailand, Vietnam and Laos were identified as the highest risk countries for
DF in the Asia-Pacific region and there was a southward expansion in the geographic
distribution, which might originate from Philippines and Thailand. Historically, the
first severe DF outbreak occurred in Manila, Philippines in 1953 followed by
Bangkok, Thailand (Gubler, 2011, Guzman et al., 2010). A southward expansion in
DF transmission was also observed in Argentina and Australia (Woodruff et al.,
2006, Carbajo et al., 2012, Hu et al., 2011). Little is known about the causes of this
southward spread of DF. Global climate change was mentioned as one of the reasons
for this southward expansion (Hu et al., 2011). In the past 100 years, mean surface
temperature has increased by 0.3-0.8 oC across the continent (IPCC, 2007), which
could create climatic conditions suitable for dengue mosquito vector and facilitated
DF transmission in this region (Benitez, 2009, Kyle and Harris, 2008, Hales et al.,
2002).
Chapter 6: General Discussion 112
Figure 6.1 Framework of research findings in this thesis
Results Paper 3 Results Paper 2 Results Paper 1
Spatiotemporal pattern of DF
High risk areas for DF
Asia-Pacific region
Most likely clusters
Secondary clusters
Increased incidence and increased number of affected countries
Transmission of DF
Bangladesh Dhaka
Spatiotemporal pattern of DF
High risk areas for DF
Most likely clusters
Dhaka
Decreased incidence and decreased number of affected districts
Predictive model
Projecting DF risk due to future climate change situations
Temperature Humidity
Increase DF incidence
Chapter 6: General Discussion 113
As my literature review shows, there has been limited research conducted on
spatial epidemiology of DF in Bangladesh. I examined the trends and space-time
clustering of DF transmission in Bangladesh in Chapter 4. I used district level DF
data for the entire country during 2000-2009. Dhaka district was identified as the
most likely DF cluster in Bangladesh. Several districts of the southern part of
Bangladesh were identified as secondary clusters in the years 2000-2002. Even
though there was a substantial increase in DF transmission in the Asia-Pacific region,
an opposite trend was observed in Bangladesh. The prevalence of DF declined in
Bangladesh over the last decade. It has speculated that the effective management of
DF patients according to the national dengue guidelines and huge public awareness
after the first outbreak in 2000 might have helped to reduce the DF transmission
(Rahman et al., 2002). Besides vector control measures, socio-economic changes,
population immunity and changes in viral strains and fitness may also contribute to
the decreased DF incidence (Cummings et al., 2009, Podder et al., 2006). Further
research is needed to identify possible reasons for the declining trend of DF. Dhaka
was constantly identified as the highest risk cluster for DF transmission in
Bangladesh over the study period. Dhaka is the capital and the largest city in
Bangladesh with a high population density. The suitable environmental conditions
for mosquito breeding due to the high population density and the movement of
infected individuals and/or mosquitoes into this region from overseas through air
travel may directly and/or indirectly increase the chance of DF transmission
(Sutherst, 2004).
Recently, several studies examined the spatiotemporal clustering of DF
transmission. Focal nature of DF transmission was reported by previous studies
which was also observed in my study (Hu et al., 2011, Li et al., 2012, Mammen et
al., 2008, Honorio et al., 2009, Hu et al., 2012, Wen et al., 2006). Studies conducted
in Australia and China found that the geographic range of DF transmission expanded
significantly in both countries (Hu et al., 2011, Li et al., 2012). I noticed similar
pattern in DF transmission in the Asia-Pacific pacific region. Like Dhaka district, DF
transmission was clustered in highly urbanized areas in other countries such as China
(Li et al., 2012).
114 Chapter 6: General Discussion
In order to examine the association between climatic factors and DF
transmission and to project the future risk of DF in Bangladesh based on different
climate change scenarios, I examined the effects of climate variability on DF
transmission and projected the future climate-related DF risk in Dhaka, Bangladesh
(Chapter 5). I selected Dhaka as the study location as it was identified as the highest
risk area for DF in Bangladesh in the previous chapter, but until now no projection
has been made to consider the impact of climate change on DF transmission in this
location.
The results suggest that temperature and relative humidity played crucial role
in the transmission of DF in Dhaka. Temperature influences the breeding, survival
and longevity of Aedes mosquitoes and the dengue virus replication and thereby
affects the transmission of DF (Patz et al., 2005, Hopp and Foley, 2001). Humidity
influences the incubation period of virus and biting activity of mosquitoes which
ultimately impact the likelihood of DF transmission (Patz et al., 1998). The results of
this study also show that these variables can be well used for forecasting outbreaks of
DF in Dhaka. An accurate early warning system to predict DF epidemics and
enhance the effectiveness of preventive measures largely relies on the sufficient lead
time. Therefore, it is important to understand the lag effects of climate on the DF
transmission. This study showed that monthly maximum temperature and relative
humidity affected the DF transmission through a lag of up to four months. Four
months are sufficient to warn the people about the possible disease outbreak and take
necessary measures to prevent the epidemic. Several studies have also documented
the optimal lead time to forecast DF ranged from 1to 5 months based on the climatic
condition (Hii et al., 2012a, Chen et al., 2010, Halide and Ridd, 2008).
Previous study in Dhaka reported a linear association between maximum
temperature, relative humidity and DF which is consistent with my findings (Karim
et al., 2012). They also observed the highest lag effects at two months which is
similar to my observation. However, Karim et al., (2012) did not account for the lag
effects of climatic factors as non-linear in their study. In my study, I found that the
lag effects of climatic variables on DF transmission are polynomial in nature.
Furthermore, I projected that DF incidence in Dhaka city will increase by more than
Chapter 6: General Discussion 115
40 times in the year 2100 compare to 2010 if the ambient temperatures increase by
3.3oC according to the IPCC regional climate projections (IPCC, 2007). I projected
the temporal risk of DF due to climate change where some studies projected the
spatial risk of DF due to changes in climatic and socio-economic factors (Astrom et
al., 2013, Hales et al., 2002). Hales et al., (2002) showed that the current
geographical limit of DF transmission can be explained with 89% accuracy on the
basis of average vapour pressure (a measure of humidity) and about 50-60% of the
projected global population will be at risk of DF by 2085 if the climate change
continues. Besides climate change, socio-economic development will influence the
spatial distribution of DF(Astrom et al., 2013). If the global climate changes as
projected and gross domestic product per capita (GDPpc) remains constant, still
global population at risk of DF will increase substantially (Astrom et al., 2013).
Adaptation to climate change might influence the likelihood of DF transmission.
Since projections for adaptation to climate change and GDPpc in Dhaka were
unavailable, I was unable to account for all these changes in my model.
6.2 IMPLICATIONS OF THE RESEARCH FINDINGS
The findings of this study may have significant implications for the planning of
public health interventions and development of DF surveillance and control policies.
The GIS technique used in this study may help the surveillance of DF and other
infectious diseases through monitoring the disease pattern over different periods of
time across different places. This technique can also help to identify high risk
communities and/or population groups and could be an important tool for the
decision makers to prioritise surveillance and disease prevention efforts into the areas
where attention is most needed. Residents and travellers to the high risk areas should
also take additional precaution to protect them from DF infection. In this study, a
scenario based projection was developed for DF in Dhaka city, which suggests that
climate change will substantially increase the DF transmission risk in Bangladesh
where the public health systems are already stretched due to the population ageing
and increased burden of disease. Therefore, it is vital information for policy makers
who want to understand the potential effects climate change on infectious diseases,
and to set priorities for mitigation and adaptation.
116 Chapter 6: General Discussion
6.3 STRENGTHS OF THIS THESIS
This thesis has three major strengths as illustrated below:
Firstly, to my knowledge this is the first study to examine the dynamic
spatiotemporal pattern of DF at a continental level. In disease control programmes,
several factors involved in the estimation of disease burden, monitoring disease
trend, identification of risk factors and planning and allocating of resources etc(Hu et
al., 2011). A common tool involved in this process is spatiotemporal trend analysis
which helps to highlight the changing disease patterns and to identify new risk
factors (Abrams and Kleinman, 2007). The risk of DF transmission varies in space
and time as mosquito density and longevity depend on a number of environmental
and ecological factors (e.g., temperature, precipitation and mosquito breeding
habitat) (Githeko, 2012). Therefore, it is important to identify the spatiotemporal
pattern of DF transmission and its determinants to predict the onset and severity of
epidemics.
Secondly, I explored high risk cluster areas for DF transmission in both the
Asia-Pacific region and Bangladesh using spatial scan statistics. Scan statistic is one
of the most commonly used approaches in spatial disease surveillance to explore
high risk areas (Kulldorff et al., 2005). Identification of high risk areas is essential
for improving disease prevention and control strategies in places where they are
mostly needed. Hence, it has wide applicability in surveillance and risk management
of DF.
Finally, I predicted the future risk of DF in Dhaka under different climate
change scenarios. This is the first attempt to make such projections in an Asian
country. There has been a need to develop an early warning system that can identify
and quantify the risk of DF outbreaks (Githeko, 2012). An early epidemic prediction
tool is critical for evaluating the risk of an outbreak that enables decision making for
Chapter 6: General Discussion 117
public health interventions. Thus, early identification of disease risk allows early
interventions and preventions of an epidemic.
6.4 LIMITATIONS OF THIS THESIS
Several limitations of this research must also be acknowledged.
Firstly, I used country level DF data for spatial analyses in the Asia-Pacific
region, which is unable to show the risk at higher resolution. Higher resolution data
(monthly administrative level 1 data) could able to show the transmission pattern
within country settings and seasonal variation.
Secondly, underreporting is likely to occur for the DF outbreak data because
the dengue notification system does not include asymptomatic patients and those
who had clinical symptom but did not seek for medical advice. This underreporting
can cause information bias in my study and under estimate the actual risk of DF
transmission. The quality of DF notification data may also vary depending on time
and location due to the difference in disease diagnosis and surveillance system. Some
developing countries in the Asia-pacific region like Vietnam, Bangladesh use only
symptomatic assessment or serological test to confirm a DF case when developed
countries (Australia) use more specific molecular test to diagnose and report a DF
case. This variation in DF reporting might cause selection bias in our study.
Finally, this study is solely based on the available routinely collected data. We
could not take into account a number of environmental factors that could influence
the relationship between climate and DF transmission like urbanization, mosquito
species and density, population immunity. Detail data on these factors are
unavailable for Dhaka city. Finally, the projection of the climate impact on DF was
only made for one city which may restrict the generalisability of the results.
118 Chapter 6: General Discussion
6.5 FUTURE RESEARCH DIRECTIONS
DF is one of the most important public health concerns in this century globally
(WHO, 2012a). The relationship between climate and DF is complex and further
research in this field is clearly warranted. This thesis has attempted to fill some
knowledge gaps but there are still many research questions should be addressed in
future research. Based on the major findings of this thesis, I would like to make the
following recommendations for future research:
6.5.1 Better understand the ecology of DF
It has been hypothesized that changes in socio-demographic features and
climate might be the possible reasons for the identified trends in DF transmission in
the Asia-Pacific region and in Bangladesh. However, how socio-economic factors
interact with climate change to influence the geographic distribution and dynamic
pattern of DF in this region remains unclear. Further research should focus on this
issue. It is also desirable to link meteorological data with long-term DF data in
different areas to examine the impact of climate and climate change on DF. If
possible, further data should be collected on important factors, such as mosquito
species and density, population immunity and mosquito control activities.
6.5.2 Advance the risk assessment techniques
Previous studies in the Asia-Pacific region and in this study, empirical
approach relying on statistical models were used to quantify the future burden of DF
due to climate change. Although, development of DF predictive model involves a
multi-disciplinary approach as climate itself cannot explain the total variation in the
disease trends (Githeko, 2012). In compare to statistical models, process-based
models, explicitly describe every aspect of disease transmission and its underlying
biological mechanisms (Bannister-Tyrrell et al., 2013). This type of models can
incorporate meteorologic information with virological, demographic and
epidemiologic data. Thus, the DF model may further improve by taking process
based approach.
Chapter 6: General Discussion 119
6.5.3 Improve disease surveillance and monitoring
The quality of DF surveillance data varies with country and time. Some
countries do not have long-term consistent surveillance data. The current surveillance
should be strengthened to increase the accuracy of surveillance data. The likelihood
of under-reporting and over-reporting bias in the dataset should be examined
rigorously.
6.5.4 Evaluate the effectiveness of the public health interventions
The effectiveness of public health interventions can be improved by using
spatiotemporal analyses technique, which enables to identify and monitor of high
risk areas for disease transmission and targeting education campaign and vector
control at specific locations. Providing training to the medical practitioners and
public health professionals of the high risk areas can also be effective to control DF
outbreaks. Therefore, it is necessary to evaluate the effectiveness of public health
intervention for DF considering the spatiotemporal techniques.
6.5.5 Translate research into policy and risk management practice
Research to date mainly focused on the identification and quantification of the
effects of climate variation on DF, with less focus on how this information can help
to manage the future risk of DF transmission. This should be addressed urgently
specially for the vulnerable communities and population groups.
120 Chapter 6: General Discussion
6.6 CONCLUSIONS
In this study, the spatiotemporal pattern and the high risk areas for DF
transmission were explored in the Asia-Pacific region. In addition, the space-time
clustering of DF transmission was examined in Bangladesh. Results show that the
geographic range of DF in the Asia-Pacific region increased significantly over the
study period. Thailand, Vietnam and Laos were identified as the highest risk areas
and there was a southward movement observed in the transmission pattern. In
Bangladesh, Dhaka was identified as the highest risk area for DF transmission.
Unlike the Asia-Pacific region, the geographic range of DF transmission in
Bangladesh reduced in recent years. The effects of climate variability on DF
transmission were examined and the future climate-related DF risks in Dhaka were
projected. The results show that climate variability (particularly maximum
temperature and relative humidity) was positively associated with DF transmission in
Dhaka. The delayed effects of climatic factors were observed at four months, which
may be applicable for warning people about the possible disease outbreak and take
necessary measures to prevent the epidemic. Assuming a temperature increase of 3.3 oC without any adaptation measures and constant socio-economic conditions by
2100, the consequence will be devastating, with a projected annual increase of
16,030 cases in Dhaka. This information might be helpful for improving DF
surveillance and control through effective vector management and community
education programs in Bangladesh and other countries of the Asia-Pacific region
with a similar situation.
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Appendices 137
Appendices
Appendix A
Conferences
“23rd Conference of the International Society for Environmental Epidemiology (ISEE)”, 13-16 September 2011, Barcelona, Spain (Oral presentation/ presented by Shilu Tong: Space-Time clusters of dengue fever in Bangladesh.)
“2010 International Climate change Adaptation Conference” NCCARF and CSIRO, 29 June -1st July 2010, Gold Coast, (Poster presentation: Impact of climate change and socio-environmental factors on dengue transmission in the Asia-Pacific region)
“2009 Queensland Institute of Health Conference” 30 November-1st December, Townsville, Australia.
“IHBI inspire 2009 Post graduate student conference”, Queensland University of Technology, 17-18 November 2009, Brisbane, Australia.
138 Appendices
Appendix B
Publications
1. Banu S, Hu W, Hurst C, Guo Y, Islam Z M &Tong S, 2012. Space-time clusters of dengue fever in Bangladesh. Tropical Medicine and International Health 17(9): 1086-1091.
2. Banu S, Hu W, Hurst C &Tong S, 2011. Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors. Tropical Medicine and International Health 16: 598-607.
Appendices 139
Appendix C