Using ecological niche modelling for mapping the risk of Rift Valley fever in Kenya
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The document discusses the use of ecological niche modeling to map the risk of Rift Valley Fever (RVF) in Kenya, detailing the disease's history, environmental factors, and methodology employed in the study. It highlights the effectiveness of the models in identifying potential high-risk areas for the disease, using various environmental data and algorithms for evaluating predictions. The conclusion emphasizes the significance of this research for policymakers in planning for future outbreaks.
Using ecological niche modelling for mapping the risk of Rift Valley fever in Kenya
- 1. Using ecological niche modelling for mapping the risk of Rift Valley fever in Kenya Purity N. Kiunga(UoN/ILRI); Philip M. Kitala (UoN); K.A. Kipronoh (KARI); Gladys Mosomtai (ICIPE); Jusper Kiplimo (ILRI) and Bernard Bett (ILRI) Regional Conference on Zoonotic Diseases in Eastern Africa Naivasha, Kenya 9-12 March 2015 The Sch
- 2. Geographical area of study: Kenya
- 3. Outline • Background and objectives • Methodology • Outputs • Discussion • Conclusion
- 4. Background Rift Valley fever (RVF) is an acute febrile arthropod- borne zoonotic disease Aetiology: RVFV, family Bunyaviridae, genus Phlebovirus RVF history in Kenya 1912: First report of RVF-like disease in sheep 1931: Virus isolation and confirmation (Daurbney et al. 1931) 2006/2007: Last outbreak in Kenya
- 5. Background • RVF NICHE El Niño/Southern Oscillation (ENSO) –causing flooding soil types- solonetz, solanchaks, planosols Elevation-less than 1100m asl Natural Difference Vegetation Index (NDVI)- 0.1 units more than 3 months Vector- Aedes ,Culicine and others Temperature (Linthicum et al. 1999; Anyamba et al. 2009; Hightower et al. 2012; Bett et al. 2013)
- 6. Objective Map RVF potential distribution Disease occurrence maps This study used Ecological Niche Modelling: •Uses presence data • Shows potential areas where RVF can occur
- 8. Methodology >ENM Strategy for estimating the actual or potential geographic distribution of a species; is to characterize the environmental conditions that are suitable for the species and then identify where suitable environments are distributed in space l
- 9. Methodology
- 10. ENM Environmental layers – Land use and land cover maps – Precipitation – NDVI – Temperature – Elevation – Soil types Occurrence data -Data describing the known distribution of a species (RVF) exist in a GIS format – point data (lat, long)
- 11. ENM Algorithms(GARP) Genetic Algorithm for Rule set Production (GARP); an open modeller software creates ecological niche models for species GARP algorithm was used to map the actual and potential distribution of Rift Valley fever distribution in Kenya and result compared to Random Forest Cover Uses rules of selection, evaluation, testing and incorporation or rejection in modelling
- 12. ENM Evaluation Assess the accuracy (Confusion matrix) •Area Under Cover (AUC) >Defined by plotting sensitivity against 1 specificity across the range of possible thresholds of 0.82 (Swets 1988 and Manel et al. 2001; AUC of 0.5 – 0.7=poor, 0.7 – 0.9=moderate and >0.9 is high performance) •A Partial receiver operating characteristic (ROC) analyses AUC prediction with a value of 1.77 (0= not good, at 1.0=very good and 2.0=excellent)
- 13. ENM •ENM output was compared with Random forest (covers more spatial areas and shows consistency) •Jackknife analysis=Variable analysis
- 14. LOGIT •2 years data (2006-07) •Case-control design cases(grid 25 by 25km +ve (20%) control) •Done to rank variables contributing to output •Input (soil, rain, NDVI, elevation, temperature)
- 15. Output
- 16. Jackknife Output NDVI variables
- 17. Jackknife output rainfall and temperature 0 10 20 30 40 50 60 70 80 90 100 OCT NOV DEC JAN FEB MAR RAINFALL TEMPERATURE
- 18. Discussion •ENM only shows spatial distribution areas of the disease but doesn’t show variable contribution to output (correlation) •Show potential and high risk areas where disease can occur •Both models important shows consistency •Logit done= ranks variable contribution to output and shows relationship between variables
- 19. Conclusion This will help policymakers to know which areas to focus their attention and put plans in place when the outbreak occurs again
- 20. THANK YOU
- 21. The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI. better lives through livestock ilri.org