1. Climate Change, GIS, and Vector- Borne Disease Jessica Beckham February 10, 2011

2. Climate change and human health: present and future risks  Anthropogenic greenhouse gas emissions  Increased temp by 2100 1.4-5.8 C  Increased annual global rainfall  Drier climate in some regions  More severe flooding and precipitation in other regions  ENSO

5. Effects on Vector-borne Infections  Incidence  Seasonal transmission  Geographic Range  Social, economic, topographic conditions?

6. Range Predictions of Aedes aegyptii

7. The El Nino Southern Oscillation and malaria epidemics in South America  Possibility of using ENSO forecasts to improve malaria control  Statistical methods look for relationship between ENSO and malaria epidemics  1956-1998  Malaria associated with flooding AND drought  Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname, Venezuela, Brazil

9. Four Null Hypotheses: Malaria epidemics are not more likely to occur in a) Nino vs. other years b) Nino +1 vs. other years c) either Nino or Nino+1 vs. other years d) La Nina vs. other years Test: Fisher’s exact (contingency table) p-value: probability that variation is due to random chance; level of significance = 0.05

10. Results

12. The El Nino Southern Oscillation and the historic malaria epidemics on the Indian subcontinent and Sri Lanka  Punjab region and former Ceylon, a.k.a. Sri Lanka  1868-1943  Periodic epidemics associated with ENSO/ monsoon fluctuations

14. Punjab Rainfall

15. Sri Lanka Rainfall

16. Monsoon Rainfall (June-Sept) Monsoon rainfall = sum of all weighted values from meteorological stations divided by area of subdivision for each year

17. “Epidemic Figure”  Avg monthly “fever deaths” Oct-Dec: Avg monthly fever deaths April-July  Administrative records  Oct-Dec = most malaria deaths occur  1867-1943 EFs calculated  EF over 2 = epidemic year  “fever deaths” due to other causes?

18. Malaria Epidemic Years -”epidemic figure” -Chi-Square Test Statistic; 1 df -in Punjab, 47.6% prob of epidemic in Nino+1 yr vs. 10.7% in other yrs (chi-square = 12.6; p<0.001) (time period 1867-1943; 1905-1943 showed no significant difference) -in Sri Lanka, 45.0% prob of epidemic in Nino yrs vs. 12.5% in other yrs (chi-square = 9.43; p<0.005)

19. Conclusions  Malaria mortality and elevated monsoon rainfall correlated with ENSO  Dry year followed by wet year = more risk for epidemic  Economic stress = more risk for epidemic

20. Prediction of a Rift Valley Fever outbreak  RVF = viral, mosquito-borne  Affects humans, domestic animals  Outbreaks linked to ENSO  Horn of Africa  Dambos  Satellite remote sensing data  Risk Map for 2006-2007

21. N

22. SST Anomalies

23. OLR Anomalies

24. Rainfall Anomalies

25. Vegetation (NDVI) Anomalies

27. RVF Risk Map

28. Conclusions  Rainfall and vegetation anomalies = ideal conditions for mosquito vector  Predicted RVF outbreak in Horn of Africa (64% of cases fell within risk map area)  Implications for future