Perspectives of predictive epidemiology and early warning systems for Rift Valley fever in Garissa, Kenya
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The document discusses predictive epidemiology and early warning systems for Rift Valley Fever (RVF) in Garissa, Kenya, detailing its etiology, economic impact, and significant risk factors associated with epidemics. It highlights the importance of environmental conditions such as rainfall and vegetation in predicting RVF outbreaks and outlines current research methods, including participatory epidemiology and community engagement for effective disease management. The study emphasizes the need for targeted interventions and risk assessments to mitigate future RVF outbreaks through enhanced surveillance and public awareness.
Perspectives of predictive epidemiology and early warning systems for Rift Valley fever in Garissa, Kenya
- 1. Perspectives of predictive epidemiology and early warning systems for Rift Valley fever in Garissa, Kenya Nanyingi MO1,3 , Muchemi GM1, Kiama SG2,Thumbi SM5,6 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 Presented at the 47th Kenya Veterinary Association Annual Scientific Conference, Mombasa, Kenya, 25 April 2013
- 2. Etiology, Epidemiolgy and Economics of RVF 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. 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). Epidemics marked with unexplained abortions (100%) Cattle, camels, small ruminants, potential human epizootics(mild) 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
- 3. 3 Risk Factors (Ecological and Climatic) 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; Bett et al.,2012
- 4. 4 Study sites: Garissa RVF Hotspots CRITERIA Historical outbreaks in (2006- 2007) Shantabaq, Yumbis, Sankuri ,Ijara, Bura, jarajilla, Denyere Large ruminant populations Transboundary livestock trade Transhumance corridors Animal clustering at water bodies Riverine and savannah ecosysytems(vector contact rates) Sentinel herd surveillance(shanta abaq and Ijaara).
- 5. Current Research Methods : RVF Spatiotemporal Epidemiology Participatory Epidemiology: Rural appraisal and Community EWS to RVF investigated. Sero-monitoring of sentinel herds and Geographical risk mapping of RVF hotspots? Trans-boundary Surveillance for secondary foci. Disease burden analysis and predictive modeling??? Decision support tools for community utilization(Risk maps, brochures, radio…) Shanta abaq Daadab Shimbirye
- 6. RVF Participatory Community Sensitization Triangulation, Key informant interviews and Focus Group discussions on RVF and Climate Change. Disease surveillance Committees (Animal health workers ,Pastoralists , Veterinary and Public health officers) Community mapping of watering Points/Dams or “Dambos”. Socioeconomic analysis of disease impacts Livelihood analysis impacted by RVF Capacity building workshop on climate change resilience and RVF control mechanisms Information feedback mechanisms ( Schools, Churches, village meetings)
- 7. Garissa: Process based RVF Outbreak Predicitve Modelling EPIDEMIOLOGICAL DATA GEOGRAPHIC/SPAT IAL 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 correlation PREDICTIVE MODELLING LOGISTIC REGRESSION, GLM PRVF div = Prainfall + Ptemp+ PNDVI+ Psoil + Pelev Surrogate( Proxy)Predictors(variables) > dropped VECTOR PROFILE
- 8. Predictive modeling: Logistic Regression/GLM Historical RVF data (1999-2010)* Outcome: RVF cases were represent with 0 or 1(-ve/+ve) : Cases in 8 of 15 divisions (Dec 2006 –Jan 2007 outbreak) Predictors: Rainfall, NDVI, Elevation Data used: 1999 – 2010: 2160 observations Univariable analysis done in R statistical computing environment 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 Multivariable 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
- 9. 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.
- 10. Garissa: Multi 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' RVFOUTBREAKS USGS LandDAAC MODIS) 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. 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
- 11. Garissa: Rainfall Estimate Differences Jan-April 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.
- 12. Ongoing Research: RVF Outbreaks and Risk correlation Bett et al.,2012 Response can be geographically targeted (Disease Information Systems). Vaccine allocation and distribution should be site specific(cost saving mechanism) Vector surveillance for secondary foci in peri-urban locations (Vectorial competence).
- 13. RVF Monitoring and Surveillance -Community Model e-surveillance and data gathering by (Mobile phones, PDA) Community sensitization/awareness by Syndromic surveillance Dissemination of Information through community vernacular radio,SMS Aanansen et al., 2009, Madder et al., 2012 e-surveillance
- 14. RVF: Decision making Collaborative tools Veterinary ,Public Health, Agriculture,Met Universities,Research Institutions Government Vulnerable Communities CAPACITY BUILDING Risk Assessment Lab Diagnosis Information MS Simulation Exercise COMMUNICATION System Appraisal strategy Participatory message devt (FGD) Media Engagement(Radio, TV) ONE HEALTH COORDINATION DISEASE CONTROL Community Sentinel Surveillance Vaccinations and Vector Control
- 15. ACKNOWLEDGEMENTS Data and Financial Support Field work facilitation Rashid I M , Garissa Kinyua J, Garissa Asaava LL , Fafi Obonyo M, Daadab Study Participants Bulla Medina CIG, Garissa Communities: Shanta abaq,Sankuri,Daadab,Ijara,Shimbirye