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One health Perspective and Vector Borne Diseases

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One health Perspective and Vector Borne Diseases

  1. 1. VECTOR BORNE DISEASES AND ONE HEALTH One Health Triad: Understanding the Environmental, Animal-human Health and Eco-wildlife Connections Presented at KVA Nairobi CPD Meeting Friday 6th February 2015 Nanyingi Mark
  3. 3. Human Cases Wild Animal Domestic Animal C A S E S TIME Animal Amplification Human Amplification Wildlife Surveillance/ Forecasting Early Detection Control Opportunity EPIDEMIOLOGICAL TRIAD AND INTERSECTION OF ZOONOSES
  4. 4. TIME Human Cases Wild Animal Domestic Animal Animal Amplification Human Amplification C A S E S Wildlife Surveillance/ Forecasting Control Opportunity Early Detection Population Surveillance Case based Surveillance Early and effective control ONE HEALTH SURVEILLANCE APPROACHES &CONTROL STRATEGIES
  5. 5. PART I: Early warning Systems for Vector Borne Climate Sensitive Diseases to Improve Human Health (Malaria and Rift Valley Fever) Nanyingi M O, Estambale B Presented at KVA Nairobi CPD Meeting Friday 6th February 2015 Project code B20278
  6. 6. 1.0 Study Background and Rationale :  The largest health impacts from climate change occurs from vector borne diseases, with mosquito transmitted infections leading in Africa  Climate change alters disease transmission by shifting vectors geographic range and density , increasing reproductive and biting rates and vector- host contact. (Ro)  Climate change to alters land use patterns potentially influencing the mosquito species composition and population size, resulting in changes in malaria and RVF transmission.  Mathematical models for vector density and climate forecasts can predict disease outbreaks by providing early lead times.  RVF Mortality and Morbidity in Kenya (1998,2006 cycles) (discussed)  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).
  7. 7. 1.1 RVF Mortality and Outbreak Model:  Reduction of population vulnerability can be addressed through integrated assessment models which link climatic and non-climatic factors.  Basic dynamic infectious disease models to obtain the epidemic potential (EP) which can be used as an index to develop early warning tools
  8. 8. 2.0 Study Goals and Objectives :  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: To develop tools for detection of the likely occurrence of climate sensitive vector borne diseases 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.
  9. 9. Study approach and design:  A multi site longitudinal study with quarterly visits.  Determination of point prevalence of P. falciparum infections and RVF in the study population. testing will be carried out three times annually.  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)  Monthly values (rainfall, temperature, NDVI) will be plotted against logit- transformed diseases prevalence (spatial and inter-annual correlations).  Vector surveillance and risk profiling by site randomization: Habitat census, Adult and larval sampling(weighted probability index for malaria endemicity)  Molecular characterization (PCR) and Phylogenetic tree linkage to risk and vector density-distribution maps.  Arboviral Pathogen discovery (AVID-Google)- ILRI/ICIPE/CDC
  10. 10. PART II: Modeling vertical transmission in vector-borne diseases with applications to Rift Valley fever in Garissa, Kenya Nanyingi M O, Thumbi SM, Kiama SG,Muchemi GM,Njenga KN, Bett B Project code: C-9650-15
  11. 11. 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.  RVFV is an OIE transboundary high impact pathogen and CDC category A select agent.  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)  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.
  12. 12. 12  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
  13. 13. 13  RVF vectors > 30 species of mosquitoes,two types: Aedes(floodwater) and Culex.  The spread and persistence of RVF is due vertical transmission from Aedes adults to eggs which need to be dry for several days before they maturity. After maturing, they hatch during the next flooding event.  Most prediction models predict RVF risk based on meteorological and climatic data while explicitly ignoring human and livestock populations. GOAL: To understand the underlying dynamics of RVF and determine the importance of vertical transmission in the persistence of RVF between epidemics.
  14. 14. Process based RVF Outbreak Predictive 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 auto correlation PREDICTIVE MODELLING LOGISTIC REGRESSION, GLM PRVF div = Prainfall + Ptemp+ PNDVI+ Psoil + Pelev Analysis of Spatial autocorrelation of serological incidence data VECTOR PROFILE
  15. 15. Compartmental Model: Ordinary Differential Equation Chitnis et al 2006; Herd Immunity
  16. 16. r h Culex eggs Aedes eggs t0Jan Dec t20 h Aedes eggs r Culex eggs t0 Jan Dec AdultDensityAdultDensity
  17. 17. Sensitivity analysis for Ro RVF Model 1: Dry season parameters with conditions perturbed to wet season. Initial conditions are Sh=1000, Ah=Ih=Rh=0, Sv=19999, Ev=0, Iv=200. Number of cattle affected = 300
  18. 18. Discussion and Conclusion  Potential for epidemicity of the system is sensitive to the vector-to – host ratio, mosquito biting rate and probability of transmission form hosts to vectors. Sensitivity analysis suggest that control methods may vary depending on season and whether the goal is to reduce initial spread and endemicity or epidemicity(reactivity). Vertical transmission of mosquitoes is often ignored in models for mosquito-borne pathogens however it plays a significant role in long term persistence of a pathogen. There is need to explore further vertical transmission rates and egg survival for modelling perspectives. Future considerations of including vector profile data as interepidemic persistence of RVF can be highly sensitive to vertical transmission rates
  19. 19. PART III: Modelling spatial and temporal distribution of Rift Valley Fever Vectors in Kenya Nanyingi M O, Thumbi SM, Kiama SG,Muchemi GM,Bett B,Munyua P, Njenga KN
  20. 20.  RVF is broadening its geographic range in Kenya with potentially significant burden on animal and human health. Previous RVF predictive models have factored in climatic and environmental variables to forecast occurrence.  This will be first attempt at a national level to create RVF vector surveillance system and predictive risk maps for Kenya using vector distribution profile to guide in strategic surveillance and control strategies. “Mosquitoes, flies, ticks and bugs may be a threat to your health – and that of your family - at home and when travelling. This is the message of this year’s World Health Day, on 7 April.”
  21. 21.  To evaluate the correlation between mosquito distribution and environmental-climatic attributes favoring emergence of RVF. (Statistical modeling the climatic, ecological and environmental drivers of RVF outbreaks).  To develop a risk map for spatial prediction of RVF outbreaks in Kenya based on potential vector distribution (Spatial and temporal analysis and risk modelling by GIS Analysis)
  22. 22. 1. Maximum Entropy(Maxent) 2. Genetic Algorithm for Rule-Set Prediction(GARP)** 3. Boosted Regression Trees(BRT) 4. Random Forest (RF) Spatial analyst tool in ArcGIS and R statistical modeling
  23. 23. Maximum Entropy (Maxent) Model Culex species was highly influence by the number of dry months variable (dm), mean annual rainfall (bio12), Aedes was influenced by rainfall derived variables
  24. 24. Boosted Regression Trees(BRT) Number of dry months (dm), longest dry seasons (llds) and rainfall of wettest month (bio 13), had the highest influence on culex species distribution.
  25. 25. Comparative Random Forest(RF) Output Aedes is highly influence by moisture index of moist quarter (mimq) rainfall of driest quarter (bio 17), rainfall of wettest month (bio13).
  26. 26. What Next?? Regional Models = Model Validation Multisite country level surveillance coupled with RVF seroepidemiology profiles for hotspots is promising for validation and genomic pathogen discovery. Maxent Geographically linked phylogenetic models?
  27. 27. 27 Limitations of the study  Lack of data from “hotspots” may complicate conclusive associations between the vector presence, epidemiological data and ecological predictors.  Temporal and spatial distribution was not explicitly examined due to insufficient vector presence data.  Lack of reliable climatic and ecological parameters from local databases hence leading to risk generalization projected from the regional- global databases.  Despite excellent model agreement in prediction of habitat suitability for vectors, species taxonomic identification is underway for specific niche modelling.  Overfitting due to clustered sampling can lead to misinterpretation of geographical spread of vector( corrected by stratification and cross- validation)
  28. 28. 28 Conclusions and Recommendations  This is an empirical attempt to predict large-scale country level spatial patterns of RVF occurrence using vector data and ecological predictor variables.  The vector predictive risk maps will be useful to animal and human health decision-makers for planning surveillance and control in RVF known high-risk areas.  The forecasting and early detection of RVF outbreaks using VSS contributes to comprehensive risk assessment of pathogen diffusion to naive areas, hence essential in disease control preparedness.  GIS tools and ENM can contribute to existing model frameworks for mapping the areas at high risk of RVFV and other vector borne diseases.
  29. 29. Future of vector risk mapping using secondary data IN-SITU RS DATAENTOMOLOGICAL DATA Hazard and Vulnerability Maps (Environmental Risk) ZPOM  Presence(Map Breeding sites)  Abundance (Density)  Host contact = Animal/humans  Precipitation (WorldClim)  Land cover (SPOT 7)  Soil types  Elevation (DEM)  NDVI Humans Livestock (Ruminant) VECTOR RISK MAP RVF OCCURRENCE DATA Tourre YM (2009) Global Health Action. Vol.2
  30. 30. Data sources  AFRICLIM database  World Clim - Global Climate data, available at http://www.worldclim.org/ Collaborating Institutions DVS, DDSR,DVBD,MOPH, ZDU,USAMRU Individuals  IHAP team, study participants, CHW, Local administrators Contact : mnanyingi@kemricdc.org, mnanyingi@gmail.com