Perspecves	  of	  Predicve	  Epidemiology	  and	  Early	  Warning	  Systems	  for	  Ri9	  Valley	  Fever	  in	  Garissa,	 ...
Etiology, Epidemiolgy and Economics of RVFMontgomery , 1912, Daubney 1931, Davies 1975, Jost et al.,2010q  RVF viral zoon...
3Risk Factors (Ecological and Climatic) 	  q  Precipitation: ENSO/Elnino above averagerainfall leading hydrographical mod...
4Study sites: Garissa RVF Hotspots 	  25th April 2013, KVA Scientific Conference, Whitesands Hotel, MombasaCRITERIAq  His...
Current Research Methods : RVF SpatiotemporalEpidemiology 	  q  Participatory Epidemiology: Ruralappraisal and Community ...
RVF Participatory Community Sensitization	  q  Triangulation, Key informant interviews andFocus Group discussions on RVF ...
Garissa:	  Process	  based	  RVF	  Outbreak	  Predicitve	  Modelling	  25th April 2013, KVA Scientific Conference, Whitesa...
Predictive modeling: Logistic Regression/GLM	  25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasaq  Hi...
Correlation Analysis: NDVI vs Rainfall	  25th April 2013, KVA Scientific Conference, Whitesands Hotel, MombasaPearsons cor...
Garissa:	  Mul	  year	  NDVI	  Comparison(2006/2007/2012)	  	  25th April 2013, KVA Scientific Conference, Whitesands Hote...
Garissa:	  Rainfall	  Esmate	  Differences	  Jan-­‐April	  2013	  25th April 2013, KVA Scientific Conference, Whitesands Ho...
Ongoing Research: RVF Outbreaks and Risk correlation 	  Be]	  et	  al.,2012	  25th April 2013, KVA Scientific Conference, ...
RVF Monitoring and Surveillance -Community Model	  q 	  	  e-­‐surveillance	  and	  data	  gathering	  by	  (Mobile	  pho...
RVF: Decision making Collaborative tools 	  Veterinary	  ,Public	  Health,	  Agriculture,Met	  	  Universi?es,Research	  I...
ACKNOWLEDGEMENTS	  Data	  and	  Financial	  Support	  	  Field	  work	  facilitaon	  	  q 	  Rashid	  I	  M	  ,	  Garissa...
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Perspectives of Predictive Epidemiology and Early Warning Systems for Rift Valley Fever in Garissa, Kenya

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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.

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Perspectives of Predictive Epidemiology and Early Warning Systems for Rift Valley Fever in Garissa, Kenya

  1. 1. Perspecves  of  Predicve  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 B41Department of Public Health, Pharmacology and Toxicology, Faculty of Veterinary Medicine, University of Nairobi PO BOX PO BOX29053-0065 Nairobi, Kenya2 Wangari Maathai Institute for Environmental Studies and Peace, College of Agriculture and Veterinary Science, University of NairobiPO BOX 30197 Nairobi, Kenya3 Colorado State University, Livestock-Climate Change Collaborative Research Support Program, CO 80523-1644,USA4International Livestock Research Institute (ILRI), Naivasha Road, P.O. Box 30709 Nairobi 00100, Kenya5Kenya Medical Research Institute, US Centres for Disease Control and Prevention, PO Box 1578 Kisumu6 Paul G Allen Global Animal Health, PO Box 647090, Washington State University, Pullman WA 99164-7090,509-335-248925th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
  2. 2. Etiology, Epidemiolgy and Economics of RVFMontgomery , 1912, Daubney 1931, Davies 1975, Jost et al.,2010q  RVF viral zoonosis of cyclic occurrence(5-10yrs), Described In Kenya in1912 isolated in 1931 in sheep with hepatic necrosis and fatal abortions.q  Caused by a Phlebovirus virus in Bunyaviridae(Family) and transmitted bymosquitoes: 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 bilayerq  Major epidemics have occurred throughout Africa and recently ArabianPeninsula; 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 livestockmortality was Ksh 610 million, in 3.4 DALYs per 1000 people and householdcosts of about Ksh 10,00025th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
  3. 3. 3Risk Factors (Ecological and Climatic)  q  Precipitation: ENSO/Elnino above averagerainfall leading hydrographical modifications/flooding (“dambos”,dams, irrigationchannels).q  Hydrological Vector emergency: 35/38spp. (interepidemic transovarialmaintenance 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 aslLinthicum et al., 1999; Anyamba et al., 2009; Hightower et al., 2012; Bett et al.,201225th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
  4. 4. 4Study sites: Garissa RVF Hotspots  25th April 2013, KVA Scientific Conference, Whitesands Hotel, MombasaCRITERIAq  Historical outbreaks in (2006-2007)q  Shantabaq, Yumbis,Sankuri ,Ijara, Bura, jarajilla,Denyereq  Large ruminant populationsq  Transboundary livestock tradeq  Transhumance corridorsq Animal clustering at water bodiesq  Riverine and savannahecosysytems(vector contact rates)q  Sentinel herd surveillance(shantaabaq and Ijaara).
  5. 5. Current Research Methods : RVF SpatiotemporalEpidemiology  q  Participatory Epidemiology: Ruralappraisal and Community EWS to RVFinvestigated.q  Sero-monitoring of sentinel herds andGeographical risk mapping of RVFhotspots?q Trans-boundary Surveillance forsecondary foci.q  Disease burden analysis andpredictive modeling???q  Decision support tools for communityutilization(Risk maps, brochures, radio…)25th April 2013, KVA Scientific Conference, Whitesands Hotel, MombasaShanta abaqDaadabShimbirye
  6. 6. RVF Participatory Community Sensitization  q  Triangulation, Key informant interviews andFocus Group discussions on RVF and ClimateChange.q  Disease surveillance Committees (Animalhealth workers ,Pastoralists , Veterinary andPublic health officers)q  Community mapping of watering Points/Dams or “Dambos”.q  Socioeconomic analysis of disease impactsq  Livelihood analysis impacted by RVFq  Capacity building workshop on climatechange resilience and RVF controlmechanismsq  Information feedback mechanisms( Schools, Churches, village meetings)25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
  7. 7. Garissa:  Process  based  RVF  Outbreak  Predicitve  Modelling  25th April 2013, KVA Scientific Conference, Whitesands Hotel, MombasaEPIDEMIOLOGICAL  DATA  GEOGRAPHIC/SPATIAL  DATA  Remote  Sensing/GIS  NDVI,  Soil,  Eleva?on  TEMPORAL  DATA  Time  Series  Rainfall,  Temperature,  NDVI  OUTCOMES:  SEROLOGICAL  DATA  (case  definion)  PCR/ELISA(IgM,  IgG)  Morbidity,  Mortality,    SOCIOECONOMIC  DATA  Par?cipatory    Interven?onal  costs,  Demographics,  Income,  Assets,    CORRELATIONAL  ANALYSIS    Spatial correlationPREDICTIVE  MODELLING  LOGISTIC  REGRESSION,  GLM      PRVFdiv = Prainfall + Ptemp+ PNDVI+ Psoil + PelevSurrogate( Proxy)Predictors(variables) > droppedVECTOR  PROFILE  
  8. 8. Predictive modeling: Logistic Regression/GLM  25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasaq  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, Elevationq  Data used: 1999 – 2010: 2160 observationsq  Univariable analysis done in R statistical computing environmentq  model <- glm(case ~ predictor, data, family=“binomial”)- 6 modelsVariable(( 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  Mulvariable  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. 9. Correlation Analysis: NDVI vs Rainfall  25th April 2013, KVA Scientific Conference, Whitesands Hotel, MombasaPearsons correlation coefficient (r) = 0.458NDVI= 0.411+ 0.764 × rainfall, p< 0.001Linear relationship between rainfall and NDVI: it is thus possible to utilizethese factors to examine and predict spatially and temporally RVFepidemics.
  10. 10. Garissa:  Mul  year  NDVI  Comparison(2006/2007/2012)    25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa16-dayNDVIataresolutionof250m0  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 NDVIunits) for 3 months would create the ecological conditions necessary forlarge scale mosquito vector breeding and subsequent transmission of RVFvirus to domestic animals and humans.q  Climatic seasonal calendar concurrence with KMD (OND) short rainsand 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. 11. Garissa:  Rainfall  Esmate  Differences  Jan-­‐April  2013  25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa!Dekadalprecipitationona0.1x0.1deg.lat/longdCPC/FEWS  RFE2.0*The short-term average may provide insight into changes in RVF risk in areaswhere precipitation anomalies are the principal cause of RVF epidemics byincrease vector competence.
  12. 12. Ongoing Research: RVF Outbreaks and Risk correlation  Be]  et  al.,2012  25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasaq  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).
  13. 13. 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
  14. 14. 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  § Informaon  MS  § Simulaon  Exercise  COMMUNICATION    § System  Appraisal  strategy    § Parcipatory  message  devt  (FGD)  § Media  Engagement(Radio,  TV)  ONE  HEALTH    COORDINATION  DISEASE  CONTROL    § Community  Sennel  Surveillance  §   Vaccinaons  and  Vector  Control      25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
  15. 15. ACKNOWLEDGEMENTS  Data  and  Financial  Support    Field  work  facilitaon    q   Rashid  I  M  ,  Garissa  q   Kinyua  J,  Garissa  q   Asaava  LL  ,  Fafi  q   Obonyo  M,  Daadab  Study  Parcipants    q   Bulla  Medina  CIG,  Garissa    q   Communi?es:  Shanta  abaq,Sankuri,Daadab,Ijara,Shimbirye  25th April 2013, KVA Scientific Conference, Whitesands Hotel, Mombasa
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