VECTOR BORNE DISEASES AND ONE HEALTH

One Health Triad: Understanding the Environmental, Animal-human Health and Eco-wildlife Connections

Presented at KVA Nairobi CPD Meeting Friday 6th February 2015

Nanyingi Mark

GLOBAL MORTALITY DISTRIBUTION DUE VECTOR BORNE DISEASES

Human Cases

Wild Animal

Domestic Animal

C

A

S

E

S

TIME

Animal

Amplification

Human

Amplification

Wildlife Surveillance/

ForecastingEarly

DetectionControl

Opportunity

EPIDEMIOLOGICAL TRIAD AND INTERSECTION OF ZOONOSES

TIME

Human Cases

Wild Animal

Domestic Animal

Animal

Amplification

Human

Amplification

C

A

S

E

S

Wildlife Surveillance/

Forecasting Control

Opportunity

Early

Detection

Population

Surveillance

Case based

Surveillance

Early and

effective control

ONE HEALTH SURVEILLANCE APPROACHES &CONTROL STRATEGIES

PART I:

Early warning Systems for Vector Borne Climate Sensitive Diseases to Improve Human Health

(Malaria and Rift Valley Fever)

Nanyingi M O, Estambale B

Presented at KVA Nairobi CPD Meeting Friday 6th February 2015

Project code B20278

1.0 Study Background and Rationale : The largest health impacts from climate change occurs from vector borne

diseases, with mosquito transmitted infections leading in Africa

Climate change alters disease transmission by shifting vectors geographic

range and density , increasing reproductive and biting rates and vector- host

contact. (Ro)

Climate change to alters land use patterns potentially influencing the

mosquito species composition and population size, resulting in changes in

malaria and RVF transmission.

Mathematical models for vector density and climate forecasts can predict

disease outbreaks by providing early lead times.

RVF Mortality and Morbidity in Kenya (1998,2006 cycles) (discussed)

In 2011,3.3 billion persons were at risk of acquiring malaria. 216 million

people developed clinical malaria in 2010 (81% in Africa), and 655,000 died

(91% in Africa, most being children).

1.1 RVF Mortality and Outbreak Model:

Reduction of population vulnerability can be addressed through integrated

assessment models which link climatic and non-climatic factors.

Basic dynamic infectious disease models to obtain the epidemic potential

(EP) which can be used as an index to develop early warning tools

2.0 Study Goals and Objectives : 2.1 Goal: To develop a framework for integrated early warning

system for improved human health and resilience to climate–

sensitive vector borne diseases in Kenya.

2.1 Objectives:

To develop tools for detection of the likely occurrence of

climate sensitive vector borne diseases

To assess and compare the temporal and spatial

characteristics of climatic, hydrological, ecosystems, and vector

bionomics variability in Baringo and Garissa counties

3.0 Output Indicator

Geo-spatial maps of RVF-Garissa and Malaria- Baringo

overlaid with climatic and hydrological ecosystems; and

vector bionomics.

Study approach and design: A multi site longitudinal study with quarterly visits.

Determination of point prevalence of P. falciparum infections and RVF in

the study population. testing will be carried out three times annually.

A stratified random sample of 1,220 primary school children aged 5 – 15 yr,

RDT for Malaria and indirect IgG+M+A+D ELISA for RVF. Monthly case

records will be aggregated into divisions and season (rainfall) and calibrated

by total population(-ve autoregressive models)

Monthly values (rainfall, temperature, NDVI) will be plotted against logit-

transformed diseases prevalence (spatial and inter-annual correlations).

Vector surveillance and risk profiling by site randomization: Habitat census,

Adult and larval sampling(weighted probability index for malaria endemicity)

Molecular characterization (PCR) and Phylogenetic tree linkage to risk and

vector density-distribution maps.

Arboviral Pathogen discovery (AVID-Google)- ILRI/ICIPE/CDC

PART II:

Modeling vertical transmission in vector-borne diseases with applications to Rift Valley fever

in Garissa, Kenya

Nanyingi M O, Thumbi SM, Kiama SG,Muchemi GM,Njenga KN, Bett B

Project code: C-9650-15

Montgomery , 1912, Daubney 1931, Davies 1975, Jost et al., 2010

RVF viral zoonosis of cyclic

occurrence(5-10yrs), described In

Kenya in 1912 isolated in 1931 in

sheep with hepatic necrosis and

fatal abortions.

Caused by a Phlebovirus virus in

Bunyaviridae(Family) and

transmitted by mosquitoes: Aedes,

culicine spp.

RVFV is an OIE transboundary

high impact pathogen and CDC

category A select agent.

The RVFV genome contains

tripartite RNA segments designated

large (L), medium (M), and small (S)

contained in a spherical (80–120 nm

in diameter) lipid bilayer.

Major epidemics have occurred

throughout Africa and recently

Arabian Peninsula; in Egypt (1977),

Kenya (1997–1998, 2006-2007),

Saudi Arabia (2000–2001) and

Yemen (2000–2001), Sudan (2007)

and Mauritania (2010)

Economic losses in 2007 outbreak

due to livestock mortality was $10

Million , in 3.4 DALYs per 1000

people and household costs of $10

for human cases. 158 human

deaths.

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Precipitation: ENSO/Elnino above

average rainfall leading hydrographical

modifications/flooding ( dambos”,dams,

irrigation channels).

Hydrological Vector emergency: 35/38

spp. (interepidemic transovarial

maintenance by aedes 1º and culicine

2º,( vectorial capacity/ competency)

Dense vegetation cover =Persistent

NDVI.(0.1 units > 3 months)

Soil types: Solonetz, Solanchaks,

planosols (drainage/moisture)

Elevation : altitude <1,100m asl

Linthicum et al., 1999; Anyamba et al., 2009; Hightower et al., 2012

13

RVF vectors > 30 species of mosquitoes,two types:

Aedes(floodwater) and Culex.

The spread and persistence of RVF is due vertical

transmission from Aedes adults to eggs which need to be

dry for several days before they maturity. After maturing,

they hatch during the next flooding event.

Most prediction models predict RVF risk based on

meteorological and climatic data while explicitly ignoring

human and livestock populations.

GOAL: To understand the underlying dynamics of RVF and

determine the importance of vertical transmission in the

persistence of RVF between epidemics.

Process based RVF Outbreak Predictive Modelling

EPIDEMIOLOGICAL DATA

GEOGRAPHIC/SPATIAL DATA

Remote Sensing/GIS

NDVI, Soil, Elevation

TEMPORAL DATA

Time Series

Rainfall, Temperature,

NDVI

OUTCOMES:SEROLOGICAL DATA

(case definition)

PCR/ELISA(IgM, IgG)Morbidity, Mortality,

SOCIOECONOMIC DATA

Participatory

Interventional costs,

Demographics, Income, Assets,

CORRELATIONAL ANALYSIS

Spatial auto correlation

PREDICTIVE MODELLINGLOGISTIC REGRESSION, GLM

PRVFdiv = Prainfall + Ptemp+ PNDVI+ Psoil + Pelev

Analysis of Spatial autocorrelation of serological incidence data

VECTOR PROFILE

Compartmental Model: Ordinary Differential Equation

Chitnis et al 2006;

Herd Immunity

r

h

Culex

eggs

Aedes

eggs

t0Jan Dec

t20

h

Aedes

eggsr

Culex

eggs

t0

Jan Dec

Adult

Den

sity

A

dult

Den

sity

Sensitivity analysis for Ro

RVF Model 1: Dry season parameters with conditions perturbed to wet season.

Initial conditions are Sh=1000, Ah=Ih=Rh=0, Sv=19999, Ev=0, Iv=200.

Number of cattle affected = 300

Discussion and Conclusion Potential for epidemicity of the system is sensitive to the vector-to –

host ratio, mosquito biting rate and probability of transmission form

hosts to vectors.

Sensitivity analysis suggest that control methods may vary depending

on season and whether the goal is to reduce initial spread and

endemicity or epidemicity(reactivity).

Vertical transmission of mosquitoes is often ignored in models for

mosquito-borne pathogens however it plays a significant role in long

term persistence of a pathogen.

There is need to explore further vertical transmission rates and egg

survival for modelling perspectives.

Future considerations of including vector profile data as interepidemic

persistence of RVF can be highly sensitive to vertical transmission rates

PART III:

Modelling spatial and temporal distribution of Rift Valley Fever Vectors in Kenya

Nanyingi M O, Thumbi SM, Kiama SG,Muchemi GM,Bett B,Munyua P,

Njenga KN

RVF is broadening its geographic range in Kenya with

potentially significant burden on animal and human health.

Previous RVF predictive models have factored in climatic

and environmental variables to forecast occurrence.

This will be first attempt at a national level to create RVF

vector surveillance system and predictive risk maps for

Kenya using vector distribution profile to guide in strategic

surveillance and control strategies.

“Mosquitoes, flies, ticks and bugs may be a threat to your health –

and that of your family - at home and when travelling. This is the

message of this year’s World Health Day, on 7 April.”

To evaluate the correlation between mosquito distribution

and environmental-climatic attributes favoring emergence of

RVF.

(Statistical modeling the climatic, ecological and

environmental drivers of RVF outbreaks).

To develop a risk map for spatial prediction of RVF

outbreaks in Kenya based on potential vector distribution

(Spatial and temporal analysis and risk modelling by GIS

Analysis)

1. Maximum Entropy(Maxent)

2. Genetic Algorithm for Rule-Set Prediction(GARP)**

3. Boosted Regression Trees(BRT)

4. Random Forest (RF)

Spatial analyst tool in ArcGIS and R statistical modeling

Maximum Entropy (Maxent) Model

Culex species was highly influence by the number of dry months variable (dm),

mean annual rainfall (bio12), Aedes was influenced by rainfall derived variables

Boosted Regression Trees(BRT)

Number of dry months (dm), longest dry seasons (llds) and

rainfall of wettest month (bio 13), had the highest influence on

culex species distribution.

Comparative Random Forest(RF) Output

Aedes is highly influence by moisture index of moist quarter (mimq)

rainfall of driest quarter (bio 17), rainfall of wettest month (bio13).

What Next?? Regional Models = Model Validation

Multisite country level surveillance coupled with RVF

seroepidemiology profiles for hotspots is promising for

validation and genomic pathogen discovery.

Maxent

Geographically linked phylogenetic models?

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Limitations of the study

Lack of data from “hotspots” may complicate conclusive

associations between the vector presence, epidemiological data and

ecological predictors.

Temporal and spatial distribution was not explicitly examined due to

insufficient vector presence data.

Lack of reliable climatic and ecological parameters from local

databases hence leading to risk generalization projected from the

regional- global databases.

Despite excellent model agreement in prediction of habitat suitability

for vectors, species taxonomic identification is underway for specific

niche modelling.

Overfitting due to clustered sampling can lead to misinterpretation of

geographical spread of vector( corrected by stratification and cross-

validation)

28

Conclusions and Recommendations

This is an empirical attempt to predict large-scale country

level spatial patterns of RVF occurrence using vector data

and ecological predictor variables.

The vector predictive risk maps will be useful to animal

and human health decision-makers for planning

surveillance and control in RVF known high-risk areas.

The forecasting and early detection of RVF outbreaks

using VSS contributes to comprehensive risk assessment

of pathogen diffusion to naive areas, hence essential in

disease control preparedness.

GIS tools and ENM can contribute to existing model

frameworks for mapping the areas at high risk of RVFV

and other vector borne diseases.

Future of vector risk mapping using secondary data

IN-SITU RS DATA ENTOMOLOGICAL DATA

Hazard and Vulnerability Maps

(Environmental Risk)

ZPOM

Presence(Map Breeding sites)

Abundance (Density)

Host contact = Animal/humans

Precipitation (WorldClim)

Land cover (SPOT 7)

Soil types

Elevation (DEM)

NDVI

Humans Livestock

(Ruminant)

VECTOR RISK MAP

RVF OCCURRENCE DATA

Tourre YM (2009) Global Health Action. Vol.2

Data sources

AFRICLIM database

World Clim - Global Climate data, available at http://www.worldclim.org/

Collaborating Institutions

DVS, DDSR,DVBD,MOPH, ZDU,USAMRU

Individuals

IHAP team, study participants, CHW, Local administrators

Contact : mnanyingi@kemricdc.org, mnanyingi@gmail.com