The Socio-Economics and Burden Impact of Rift Valley Fever in Garissa, Kenya
Perspectives of Predictive Epidemiology and Early Warning Systems for Rift Valley Fever in Garissa,...
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Perspec’ves of Predic’ve Epidemiology and Early Warning Systems for Ri9 Valley Fever in Garissa, Kenya Nanyingi M O 1,3 , Muchemi G M 1 , Kiama S G 2 ,Thumbi S M 5,6 and Bett B 4 1 Department of Public Health, Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Nairobi PO BOX PO BOX 29053-0065 Nairobi, Kenya 2 Wangari Maathai Institute for Environmental Studies and Peace, College of Agriculture and Veterinary Science, University of Nairobi PO BOX 30197 Nairobi, Kenya 3 Colorado State University, Livestock-Climate Change Collaborative Research Support Program, CO 80523-1644,USA 4 International Livestock Research Institute (ILRI), Naivasha Road, P.O. Box 30709 Nairobi 00100, Kenya 5 Kenya Medical Research Institute, US Centres for Disease Control and Prevention, PO Box 1578 Kisumu 6 Paul G Allen Global Animal Health, PO Box 647090, Washington State University, Pullman WA 99164-7090,509-335-2489 25 th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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Rift Valley Fever (RVF) is an arthropod-borne viral zoonosis with a potential global threat to domestic animals and humans. Climate variability is recognized as one of the major drivers contributing RVF epidemics and epizootics that have been closely linked to cyclic occurrence of the warm phase of the El Niño southern oscillation (ENSO) phenomenon. Using retrospective reanalysis and cross sectional participatory approaches we evaluate the impacts of climate change on pastoral communities and outline their roles in community based early warning systems for RVF. We compare the spatiotemporal correlation of normalized difference vegetation index (NDVI) and Rainfall Estimate Differences as surrogate predictors of RVF outbreaks in Garissa over the past decade. A bivariate regression model to provide a month-ahead lead-time for earlier prediction of RVF is described. We also explore the recent RVF outbreaks linkage to other environmental conditions using long-term sentinel data collected on the field. The results indicate a significant correlation between elevated rainfall and NDVI (> 0.43) anomalies with recent RVF epidemics (P < 0.5). Persistent elevated rainfall and NDVI suggest that there is a likelihood of another RVF outbreak due to enhance vector competence. Given the nearly linear relationship between rainfall and NDVI it is thus possible to utilize these factors to examine and predict spatially and temporally RVF epidemics for effective surveillance with limited resources. This small-scale focal study will contribute to various existing predictive tools and present a good opportunity for preparedness and mitigation of RVF by local, national and international organizations involved in the prevention and control of RVF.
Transcript of Perspectives of Predictive Epidemiology and Early Warning Systems for Rift Valley Fever in Garissa,...
Perspec'ves of Predic've Epidemiology and Early Warning Systems for Ri9 Valley Fever in Garissa, Kenya
Nanyingi M O1,3 , Muchemi G M1, Kiama S G2,Thumbi S M5,6 and Bett B4
1Department of Public Health, Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Nairobi PO BOX PO BOX 29053-0065 Nairobi, Kenya 2 Wangari Maathai Institute for Environmental Studies and Peace, College of Agriculture and Veterinary Science, University of Nairobi PO BOX 30197 Nairobi, Kenya 3 Colorado State University, Livestock-Climate Change Collaborative Research Support Program, CO 80523-1644,USA 4International Livestock Research Institute (ILRI), Naivasha Road, P.O. Box 30709 Nairobi 00100, Kenya 5Kenya Medical Research Institute, US Centres for Disease Control and Prevention, PO Box 1578 Kisumu 6 Paul G Allen Global Animal Health, PO Box 647090, Washington State University, Pullman WA 99164-7090,509-335-2489
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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 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 Epidemics marked with unexplained abortions (100%) Cattle, camels, small ruminants, potential human epizootics(mild)
q Economic losses in Garissa and Ijara districts (2007) due to livestock mortality was Ksh 610 million, in 3.4 DALYs per 1000 people and household costs of about Ksh 10,000 25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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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; Bett et al.,2012 25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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Study sites: Garissa RVF Hotspots
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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 contact rates)
q Sentinel herd surveillance(shanta abaq and Ijaara).
Current Research Methods : RVF Spatiotemporal Epidemiology
q Participatory Epidemiology: Rural appraisal and Community EWS to RVF investigated. q Sero-monitoring of sentinel herds and Geographical risk mapping of RVF hotspots? q Trans-boundary Survei l lance for secondary foci.
q Disease burden analysis and predictive modeling???
q Decision support tools for community utilization(Risk maps, brochures, radio…)
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
Shanta abaq
Daadab
Shimbirye
RVF Participatory Community Sensitization q Triangulation, Key informant interviews and Focus Group discussions on RVF and Climate Change. q Disease surveillance Committees (Animal health workers ,Pastoralists , Veterinary and Public health officers)
q Community mapping of watering Points/Dams or “Dambos”.
q Socioeconomic analysis of disease impacts
q Livelihood analysis impacted by RVF
q Capacity building workshop on climate change res i l i ence and RVF con t ro l mechanisms
q Information feedback mechanisms ( Schools, Churches, village meetings)
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
Garissa: Process based RVF Outbreak Predicitve Modelling
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
EPIDEMIOLOGICAL DATA
GEOGRAPHIC/SPATIAL DATA
Remote Sensing/GIS
NDVI, Soil, Eleva?on
TEMPORAL DATA
Time Series
Rainfall, Temperature,
NDVI
OUTCOMES: SEROLOGICAL DATA (case defini'on)
PCR/ELISA(IgM, IgG) Morbidity, Mortality,
SOCIOECONOMIC DATA
Par?cipatory Interven?onal
costs, Demographics, Income, Assets,
CORRELATIONAL ANALYSIS
Spatial correlation
PREDICTIVE MODELLING LOGISTIC REGRESSION, GLM
PRVFdiv = Prainfall + Ptemp+ PNDVI+ Psoil + Pelev
Surrogate( Proxy)Predictors(variables) > dropped
VECTOR PROFILE
Predictive modeling: Logistic Regression/GLM
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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$$
Univariable Model
Mul'variable 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%%
Correlation Analysis: NDVI vs Rainfall
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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.
Garissa: Mul' year NDVI Comparison(2006/2007/2012)
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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MA A AM M MJ J JJ J JA A AS S SO O ON ND D D2 J JF F FM M
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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
Garissa: Rainfall Es'mate Differences Jan-‐April 2013
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
!
Dekadal precipitation on a 0.1 x 0.1 deg. lat/lon gd
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.
Ongoing Research: RVF Outbreaks and Risk correlation
Be] et al.,2012 25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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 in peri-urban locations (Vectorial competence).
RVF Monitoring and Surveillance -Community Model
q e-‐surveillance and data gathering by (Mobile phones, PDA)
q Community sensi?za?on/awareness by Syndromic surveillance
q Dissemina?on of Informa?on through community vernacular radio,SMS
Aanansen et al., 2009, Madder et al., 2012
e-‐surveillance
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
RVF: Decision making Collaborative tools
Veterinary ,Public Health, Agriculture,Met Universi?es,Research Ins?tu?ons
Government
Vulnerable Communi?es 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
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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 Communi?es: Shanta abaq,Sankuri,Daadab,Ijara,Shimbirye
25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
