The document discusses developing an early warning system for vector-borne diseases like malaria and Rift Valley fever in Kenya. It aims to 1) develop tools to detect likely disease outbreaks and 2) assess climate, hydrological, ecosystem and vector factors in high risk areas. The study will analyze disease prevalence, climate/environmental data, and vector surveillance to develop maps overlaying risk factors and disease patterns. This will inform development of predictive models, identify hotspots, and support early warnings to improve human health and resilience against climate-sensitive diseases.

1. PART
I:
Early
warning
Systems
for
Vector
Borne
Climate
Sensi<ve
Diseases
to
Improve
Human
Health
(Malaria
and
Ri*
Valley
Fever)
Nanyingi M O, Kariuki N, Thumbi S,Kiama SG,Bett B, Estambale B
Presented at KEMRI-WellcomeTrust, Kilifi Scientific Meeting, Monday 24th June 2013

2. One Health and Zoonoses in Kenya
One
Health
IHAP
PBASS
VECTOR SURVEILLANCE

3. 1.0
Study
Background
and
Ra<onale
:
q The largest health impacts from climate change occurs from vector borne
diseases, with mosquito transmitted infections leading in Africa
q Climate change alters disease transmission by shifting vectors geographic
range and density , increasing reproductive and biting rates and vector- host
contact. (Ro)
q Climate change to alters land use patterns potentially influencing the
mosquito species composition and population size, resulting in changes in
malaria and RVF transmission.
q Mathematical models for vector density and climate forecasts can predict
disease outbreaks by providing early lead times.
q RVF Mortality and Morbidity in Kenya (1998,2006 cycles) (discussed)
q 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).

4. 1.1
RVF
Mortality
and
Outbreak
Model:
q Reduction of population vulnerability can be addressed through integrated
assessment models which link climatic and non-climatic factors.
q Basic dynamic infectious disease models to obtain the epidemic potential
(EP) which can be used as an index to develop early warning tools

5. 2.0
Study
Goals
and
Objec<ves
:
q 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:
q To develop tools for detection of the likely occurrence of
climate sensitive vector borne diseases
q 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.

6. Study
approach
and
design:
q A multi site longitudinal study with quarterly visits.
q Determination of point prevalence of P. falciparum infections and RVF in
the study population. testing will be carried out three times annually.
q 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)
q Monthly values (rainfall, temperature, NDVI) will be plotted against logit-
transformed diseases prevalence (spatial and inter-annual correlations).
q Vector surveillance and risk profiling by site randomization: Habitat census,
Adult and larval sampling(weighted probability index for malaria endemicity)
q Molecular characterization (PCR) and Phylogenetic tree linkage to risk and
vector density-distribution maps.
q Arboviral Pathogen discovery (AVID-Google)- ILRI/ICIPE/CDC

7. Disease Early Warning Systems (DEWS)
MEWS
(MODIS_NDVI)
Are these tools available universally and utilized adequately?
HEALTH
MAPPER

8.
PART
II:
Perspec<ves
of
Predic<ve
Epidemiology
for
RiR
Valley
Fever
in
Garissa,
Kenya
Nanyingi M O, Thumbi SM, Kiama SG,Muchemi GM,Njenga KN, Bett B
Project
code:
C-­‐9650-­‐15
Presented at KEMRI-WellcomeTrust, Kilifi Scientific Meeting, Monday 24th June 2013

9. Etiology, Epidemiolgy and Economics of RVF
Montgomery , 1912, Daubney 1931, Davies 1975, Jost et al., 2010
q RVF viral zoonosis of cyclic occurrence(5-10yrs), described In Kenya in
1912 isolated in 1931 in sheep with hepatic necrosis and fatal abortions.
q Caused by a Phlebovirus virus in Bunyaviridae(Family) and transmitted by
mosquitoes: Aedes, culicine spp.
q RVFV is an OIE transboundary high impact pathogen and CDC category A
select agent.
q 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
q 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).
q 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.

10. 10
Risk Factors (Ecological and Climatic)
q Precipitation: ENSO/Elnino above average
rainfall leading hydrographical modifications/
flooding (“dambos”,dams, irrigation
channels).
q Hydrological Vector emergency: 35/38
spp. (interepidemic transovarial
maintenance by aedes 1º and culicine 2º,
(  vectorial capacity/ competency)
q Dense vegetation cover =Persistent NDVI.
(0.1 units > 3 months)
q Soil types: Solonetz, Solanchaks,
planosols (drainage/moisture)
q Elevation : altitude <1,100m asl
Linthicum et al., 1999; Anyamba et al., 2009; Hightower et al., 2012

11. 11
Study site: Garissa RVF Hotspots
CRITERIA
q Historical outbreaks in (2006-2007)
q Shantabaq, Yumbis,
Sankuri ,Ijara, Bura, jarajilla,
Denyere
q Large ruminant populations
q Transboundary livestock trade
q Transhumance corridors
q Animal clustering at water bodies
q Riverine and savannah
ecosysytems (vector host contact
rates)
q Sentinel herd surveillance

12. Research : RVF Spatiotemporal Epidemiology
q Participatory Epidemiology: Rural
appraisal and Community EWS.
q Sero-monitoring of sentinel herds and
Geographical risk mapping of RVF
hotspots?
q Trans-boundary Surveillance for
secondary foci.
q Disease burden analysis and
predictive modeling???
q Decision support tools (Risk maps,
brochures, radio…)
q Subunit /Clone 13 Vaccine development
Shanta abaq
Daadab
Shimbirye

13. Process
based
RVF
Outbreak
Predic<ve
Modelling
EPIDEMIOLOGICAL
DATA
GEOGRAPHIC/
SPATIAL
DATA
Remote
Sensing/GIS
NDVI,
Soil,
ElevaIon
TEMPORAL
DATA
Time
Series
Rainfall,
Temperature,
NDVI
OUTCOMES:
SEROLOGICAL
DATA
(case
defini<on)
PCR/ELISA(IgM,
IgG)
Morbidity,
Mortality,
SOCIOECONOMIC
DATA
ParIcipatory
IntervenIonal
costs,
Demographics,
Income,
Assets,


CORRELATIONAL
ANALYSIS

Spatial auto correlation
PREDICTIVE
MODELLING
LOGISTIC
REGRESSION,
GLM
PRVF
div = Prainfall + Ptemp+ PNDVI+ Psoil + Pelev
Analysis of Spatial autocorrelation of serological incidence data
VECTOR
PROFILE

14. Predictive modeling: Logistic Regression/GLM
q Historical RVF data (1999-2010)*
q Outcome: RVF cases were represent with 0 or 1(-ve/+ve)
: Cases in 8 of 15 divisions (Dec 2006 –Jan 2007 outbreak)
q Predictors: Rainfall, NDVI, Elevation
q Data used: 1999 – 2010: 2160 observations
q Univariable analysis done in R statistical computing environment
q model <- glm(case ~ predictor, data, family=“binomial”)- 6 models
Variable(( Odds(Ratio(OR)( Lower(95%(CI( Upper(95%(CI( p:value(
NDVI( 1.9$ 1.40$ 2.9$ <$0.001$
Rainfall( 1.08$ 1.05$ 1.11$ <$0.001$
Elevation( 1.01$ 0.99$ 1.01$ 0.695$
$
Univariate
Model
Mul<variate
Model
Variable(( Odds(Ratio(OR)( Lower(95%(CI( Upper(95%(CI( p:value(
NDVI( 1.47% 1.05% 2.2% 0.03%
Rainfall( 1.06% 1.03% 1.09% <%0.001%
%??? Beta-binomial logistic regression model, with serologic incidence aggregated at the
compound level as the response, and the climatic metrics as the explanatory variables

15. Correlation Analysis: NDVI vs Rainfall
Pearson's correlation coefficient (r) = 0.458
NDVI= 0.411+ 0.764 × rainfall, p< 0.001
Linear relationship between rainfall and NDVI: it is thus possible to utilize
these factors to examine and predict spatially and temporally RVF
epidemics.

16. Garissa:
Rainfall
Es<mate
Differences
and
MODIS(NDVI)
2013
Dekadalprecipitationona0.1x0.1deg.lat/longd
CPC/FEWS
RFE2.0*
The short-term average may provide insight into changes in RVF risk in areas
where precipitation anomalies are the principal cause of RVF epidemics by
increase vector competence.

17. Garissa:
Mul<
year
NDVI
Comparison(2006/2007/2012)
16-dayNDVIataresolutionof250m
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
MA
A
AM
M
MJ
J
JJ
J
JA
A
AS
S
SO
O
ON
ND
D
D2
J
JF
F
FM
M
06'
07'
12'
RVF
OUTBREAKS
USGS LandDAAC MODIS)
q Persistence in positive NDVI anomalies (average greater than 0.1 NDVI
units) for 3 months would create the ecological conditions necessary for
large scale mosquito vector breeding and subsequent transmission of RVF
virus to domestic animals and humans.
q Climatic seasonal calendar concurrence with KMD (OND) short rains
and RVF alerts issued by DVS.
interannual rainfall var, NDVI of 0.43-0.45/ SST by 0.5 ° ) epidemic indicative*
* Linthicum et al ., 1999

18. Where are the Vectors?: Outbreaks and Risk correlation
Murithii
et
al
2010,
Be`
et
al.,2012
q 5 fold probability of outbreak in endemic vs non endemic (62% to 11%)
q Response can be geographically targeted (Disease Information Systems).
q Vaccine allocation and distribution should be site specific(cost saving mechanism)
q Vector surveillance for secondary foci and peri-urban locations (Vectorial
competence and capacity) for hydrological vector emergence modelling

19. RVF Monitoring and Surveillance -Community Model
q
e-­‐surveillance
and
data
gathering
by
(Mobile
phones,
Digital
pen,
PDA)
q
Community
sensiIzaIon/awareness
by
syndromic
surveillance.
q
DisseminaIon
of
InformaIon
through
community
vernacular
radio,SMS
Aanansen
et
al.,
2009,
Madder
et
al.,
2012
e-­‐surveillance

20. RVF: Decision making Collaborative tools
Veterinary
,Public
Health,
Agriculture,Met
UniversiIes,Research
InsItuIons
Government
Vulnerable
CommuniIes
CAPACITY
BUILDING
§ Risk
Assessment
§ Lab
Diagnosis
§ Informa<on
MS
§ Simula<on
Exercise
COMMUNICATION
§ System
Appraisal
strategy
§ Par<cipatory
message
devt
(FGD)
§ Media
Engagement(Radio,
TV)
ONE
HEALTH
COORDINATION
DISEASE
CONTROL
§ Community
Sen<nel
Surveillance
§
Vaccina<ons
and
Vector
Control

21. ACKNOWLEDGEMENTS
Data
and
Financial
Support
Field
work
facilita<on
q
Rashid
I
M
,
Garissa
q
Kinyua
J,
Garissa
q
Asaava
LL
,
Fafi
q
Obonyo
M,
Daadab
Study
Par<cipants
q
Bulla
Medina
CIG,
Garissa
q
CommuniIes:
Shanta
abaq,Sankuri,Daadab,Ijara,Shimbirye