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