PART	  I:	  	  Early	  warning	  Systems	  for	  Vector	  Borne	  Climate	  Sensi<ve	  Diseases	  to	  	  Improve	  Human	...
One Health and Zoonoses in Kenya 	  One	  Health	  IHAPPBASSVECTOR SURVEILLANCE
1.0	  Study	  Background	  and	  Ra<onale	  	  :	  q  The largest health impacts from climate change occurs from vector b...
1.1	  RVF	  Mortality	  and	  Outbreak	  Model:	  q  Reduction of population vulnerability can be addressed through integ...
2.0	  Study	  Goals	  and	  Objec<ves	  :	  q  2.1 Goal: To develop a framework for integrated early warningsystem for im...
Study	  approach	  and	  design:	  q A multi site longitudinal study with quarterly visits.q  Determination of point pre...
Disease Early Warning Systems (DEWS) 	  MEWS	  (MODIS_NDVI)	  Are these tools available universally and utilized adequatel...
 PART	  II:	  	  	  Perspec<ves	  of	  Predic<ve	  Epidemiology	  for	  RiR	  Valley	  Fever	  in	  Garissa,	  Kenya	  Nan...
Etiology, Epidemiolgy and Economics of RVFMontgomery , 1912, Daubney 1931, Davies 1975, Jost et al., 2010q  RVF viral zoo...
10Risk Factors (Ecological and Climatic) 	  q  Precipitation: ENSO/Elnino above averagerainfall leading hydrographical mo...
11Study site: Garissa RVF Hotspots 	  CRITERIAq  Historical outbreaks in (2006-2007)q  Shantabaq, Yumbis,Sankuri ,Ijara,...
Research : RVF Spatiotemporal Epidemiology 	  q  Participatory Epidemiology: Ruralappraisal and Community EWS.q  Sero-mo...
Process	  based	  RVF	  Outbreak	  Predic<ve	  Modelling	  EPIDEMIOLOGICAL	  DATA	  GEOGRAPHIC/SPATIAL	  DATA	  Remote	  S...
Predictive modeling: Logistic Regression/GLM	  q  Historical RVF data (1999-2010)*q  Outcome: RVF cases were represent w...
Correlation Analysis: NDVI vs Rainfall	  Pearsons correlation coefficient (r) = 0.458NDVI= 0.411+ 0.764 × rainfall, p< 0.0...
Garissa:	  Rainfall	  Es<mate	  Differences	  and	  MODIS(NDVI)	  2013	  	  Dekadalprecipitationona0.1x0.1deg.lat/longdCPC/...
Garissa:	  Mul<	  year	  NDVI	  Comparison(2006/2007/2012)	  	  16-dayNDVIataresolutionof250m0	  0.1	  0.2	  0.3	  0.4	  0...
Where are the Vectors?: Outbreaks and Risk correlation 	  Murithii	  et	  al	  2010,	  Be`	  et	  al.,2012	  q  5 fold pr...
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	  	  UniversiIes,Research	  I...
ACKNOWLEDGEMENTS	  Data	  and	  Financial	  Support	  	  Field	  work	  facilita<on	  	  q 	  Rashid	  I	  M	  ,	  Gariss...
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Early warning Systems for Vector Borne Climate Sensitive Diseases to Improve Human Health

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Early warning Systems for Vector Borne Climate Sensitive Diseases to Improve Human Health

  1. 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 BPresented at KEMRI-WellcomeTrust, Kilifi Scientific Meeting, Monday 24th June 2013
  2. 2. One Health and Zoonoses in Kenya  One  Health  IHAPPBASSVECTOR SURVEILLANCE
  3. 3. 1.0  Study  Background  and  Ra<onale    :  q  The largest health impacts from climate change occurs from vector bornediseases, with mosquito transmitted infections leading in Africaq  Climate change alters disease transmission by shifting vectors geographicrange and density , increasing reproductive and biting rates and vector- hostcontact. (Ro)q  Climate change to alters land use patterns potentially influencing themosquito species composition and population size, resulting in changes inmalaria and RVF transmission.q  Mathematical models for vector density and climate forecasts can predictdisease 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 millionpeople developed clinical malaria in 2010 (81% in Africa), and 655,000 died(91% in Africa, most being children).
  4. 4. 1.1  RVF  Mortality  and  Outbreak  Model:  q  Reduction of population vulnerability can be addressed through integratedassessment 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. 5. 2.0  Study  Goals  and  Objec<ves  :  q  2.1 Goal: To develop a framework for integrated early warningsystem 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 ofclimate sensitive vector borne diseasesq To assess and compare the temporal and spatialcharacteristics of climatic, hydrological, ecosystems, andvector bionomics variability in Baringo and Garissa counties3.0  Output  Indicator  Geo-spatial maps of RVF-Garissa and Malaria- Baringooverlaid with climatic and hydrological ecosystems; andvector bionomics.
  6. 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 inthe 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 caserecords will be aggregated into divisions and season (rainfall) and calibratedby 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 andvector density-distribution maps.q Arboviral Pathogen discovery (AVID-Google)- ILRI/ICIPE/CDC
  7. 7. Disease Early Warning Systems (DEWS)  MEWS  (MODIS_NDVI)  Are these tools available universally and utilized adequately?HEALTH  MAPPER  
  8. 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 BProject  code:  C-­‐9650-­‐15    Presented at KEMRI-WellcomeTrust, Kilifi Scientific Meeting, Monday 24th June 2013
  9. 9. 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  RVFV is an OIE transboundary high impact pathogen and CDC category Aselect 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 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  Economic losses in 2007 outbreak due to livestock mortality was $10Million , in 3.4 DALYs per 1000 people and household costs of $10 for humancases. 158 human deaths.
  10. 10. 10Risk 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
  11. 11. 11Study site: Garissa RVF Hotspots  CRITERIAq  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 host contactrates)q  Sentinel herd surveillance
  12. 12. Research : RVF Spatiotemporal Epidemiology  q  Participatory Epidemiology: Ruralappraisal and Community EWS.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 (Risk maps,brochures, radio…)q Subunit /Clone 13 Vaccine developmentShanta abaqDaadabShimbirye
  13. 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 correlationPREDICTIVE  MODELLING  LOGISTIC  REGRESSION,  GLM      PRVFdiv = Prainfall + Ptemp+ PNDVI+ Psoil + PelevAnalysis of Spatial autocorrelation of serological incidence dataVECTOR  PROFILE  
  14. 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, 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$$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 thecompound level as the response, and the climatic metrics as the explanatory variables
  15. 15. Correlation Analysis: NDVI vs Rainfall  Pearsons 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.
  16. 16. Garissa:  Rainfall  Es<mate  Differences  and  MODIS(NDVI)  2013    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.
  17. 17. Garissa:  Mul<  year  NDVI  Comparison(2006/2007/2012)    16-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
  18. 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 (Vectorialcompetence and capacity) for hydrological vector emergence modelling
  19. 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. 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. 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  

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