Post on 19-Jan-2017
transcript
Claude FLAMAND Epidemiology unit, Institut Pasteur de la Guyane
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How climate is intertwined with dengue
fever outbreaks in French Guiana
8th July 2015 - Our Common Future under Climate Change
Increasing risks of epidemics, pandemics and diseases re-emergence in
a world in constant transition
- Climate and environmental changes
- Rapid population increase and human movements
- Evolution of pathogens (resistance to antibiotics, virulence, increase, …)
Climate changes on future distribution of vector-borne diseases are an
important area of epidemiologic research
Epidemics associated with Climate Changes
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Female mosquito
Aedes sp
Virus
Flavivirus
Human host
Climate
Environment
Human
Most important mosquito-borne viral disease
- Likely more important than malaria in terms of morbidity and economic impact
Acquired through the bite of Aedes aegypti
Tropical/Subtropical area
- 3.6 billion people at risk
- 390 million dengue infections per year
Four viral serotypes (DENV1 – DENV4)
Spectrum of clinical illness
- Influenza-like illness
- Fatal dengue hemorrhagic fever (DHF)
- Dengue shock syndrome (DSS)
No vaccine, no curative treatment
Vector control and treatment strategies
Dengue fever, an escalating public health concern
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Dengue fever in French Guiana
Circulation of 4 serotypes
Evolution from endemoepidemic to hyper-endemic state
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To
tal n
um
ber o
f cases
Nu
mb
er o
f id
en
tifi
ed
sero
types
DEN-1 DEN-2 DEN-3 DEN-4 Biologically confirmed cases
Source : CNR arbovirus IPG, Cire Antilles-Guyane
2006 Outbreak (DENV2)
16 200 Clinical cases /204 hosp.
13% DHF, 4 deaths
2009 Outbreak (DENV-1, DENV-4)
13 900 CC/ 241 hosp., 1% DHF, 2 deaths
2010 : DENV-4, DENV-1
9 400 clinical cases
114 hosp., 2% DHF, 1 death
2012-2013 Outbreak (DENV2)
13 240 CC, 689 hosp (12%SD)
6 deaths
Motives and objectives
From outbreak detection to prediction
Relationship between climate and DF
- Higher occurrence of dengue in Nino years in America
- Relationship identified in FG
No climate based model for early warning system
Outbreak occurrence drivers remains poorly understood
- Niño conditions can be used as a proxi for epidemic risk ?
- Role of induced large scale atmospheric circulation modifications ?
- Impact of regional impacts (e.g. RR, Temp. ) on DF occurence ?
Investigate the impact of climate on DF outbreaks
Evaluate the potential of climate to forecast DF outbreaks
Ferreira, 2014
Gagnon, 2001
Type Source Period Time scale Spatial scale Parameters
Dengue Fever
cases INVS 1991 – 2013 Monthly French Guiana
Confirmed
cases
Gridded
atmospheric
reanalysis
ECMWF ERA-
INTERIM 1990 – 2013 6 h
Global
(0.75° x 0.75°)
Pressure
Wind speed
SST
Humidity
Meteorological
observations Meteo France 1990 – 2013 Daily Stations
Temperature
Rainfall
Humidity
Large scale
indexes NOAA 1990 - 2013 Monthly Oceans
Niño & NAO
indexes
EPI and NONEPI years ?
(tercile method on a 20-year
period)
Composite analysis
(EPI – NONEPI) over climate
parameters
Predictive Model
(Binomial logistic regression)
Data & Methods
Epidemiologic year-to-year variability and seasonnality
10 outbreaks : 1992, 1997, 1998, 2001, 2002, 2005, 2006, 2009, 2010, 2013
Annual monthly cycle showed strong seasonality
Composite analysis based on climate data
Primary assessment of climate impact on DF
July – August
Heating of the
Equatorial Pacific Sea
Surface Temperature
(SST) conditions
November
Increase in the
differences of
pressure conditions
between the Azores
High and the
Amazon Depression
Rainfall deficit over French Guiana during the dry
season preceeding the begining of outbreaks
Univariate Multivariate
Coef Std Err. p-val Coef Std Err. p-val
SLP difference Nov-1 index
By one hPa increase 0.30 0.15 0.047 0.42 0.20 0.030
SST Pacific Jul-1 Aug-1 index
By one °C increase 2.61 1.42 0.073 2.88 1.33 0.034
ROC scores
Towards a Predictive climate-based model
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Conclusion and Perspectives
First experience of forecasting DF outbreak in FG
- Good predictability obtained with a simple model
- Integrating the results in the risk assessment and preparedness
More evidences that climate becomes increasingly suitable for
dengue fever outbreaks but other factors to consider
- Increased travel, land use, vulnerability, mosquitoes resistance…
Need for multi data source simulations
- Using ensembles of disease models, climate models, population and
climate change scenarios
- To understand and anticipate indirect, long-term processes
- To evaluate the impact of mitigation strategies
Multi-disciplinary projects
- Entomologists , epidemiologists, human and animal health specialists,
climatologists-meteorologists, interface scientists, …)
www.pasteur-cayenne.fr 12
with the contribution of CNES, Aerology laboratory and Meteo France
Regional epidemiology unit of National Institute for Public Health Surveillance (Cire AG) Epi Surveillance
Medical entomology unit (IPG), IRD