1. The potential effects of climate change on malaria in tropical Africa using regionalised climate projections European Geosciences Union (EGU) General Assembly 2012 CL2.5 Climate and infectious disease interactions Volker Ermert, Andreas H. Fink, Heiko Paeth, and Andrew P. Morse Tuesday, 24 April 2012 Congress Center, Austria Center Vienna, Bruno-Kreisky-Platz 1, Room 13

2. MALARIA - one of the world’s most serious health problems ©AMMA ©Sachs & Malaney (2002) ©MARA ©mosquitomenace.com Central question: How does the spread of malaria evolve in a warmer future climate?

3. EIR a S 2005 parasite ratio PR <15 c v (PR <15 ) malaria risk Meteorological data & malaria observations Malaria simulations Present-day & projections Malaria modelling Outline of the Study LMM calibration LMM 2010 Station time series & malaria field studies Present-day climate Scenarios: A1B & B1 validation & bias- correction CRU RR ERA40 T EIR a mosquito bites malaria season MSM malaria season Ermert et al. 2011a,b Malaria Journal, 10: 35 & 62 Ermert et al. 2012a Env Health Persp, 120, 77-84 Ermert et al. 2012b, sub. to Climatic Change

4. Regionalised climate projections from the REgional MOdel (REMO) Meteorological data & malaria observations Present-day climate Scenarios: A1B & B1 validation & bias- correction CRU RR ERA40  including projected Land Use and land Cover (LUC) changes T  strong influence on the hydrological cylce  strong precipitation decline due to reduced water recycling Further details: see Paeth et al. (2009), J Clim, 22, 114-132. croplands mixed forests woody savannasurban and built-up Source: after Paeth et al. (2009), J Clim, 22, 114-132, their Fig.1

5. statistical significant at the 5% level (Wilcoxon-Mann-Whitey rank-sum test) REMO: Precipitation (RR) and change of precipitation (  RR) corrected by CRU data Source: after Ermert et al. (2012), EHP, 120, 77-84

6. REMO: Temperature (T) and temperature change (  T) corrected by ERA-40 data Source: after Ermert et al. (2012), EHP, 120, 77-84

7. monthly values MSM malaria season MARA Seasonality Model (Tanser et al. 2003) S 2005 model PR <15 P. falciparum infection model from Smith et al. 2005 Parasite Ratio of children EIR a annual Entomological Inoculation Rate (mosquito bites) daily values LMM 2010 dynamical mathematical- biological Liverpool Malaria Model (Hoshen & Morse 2004; Ermert et al. 2011a,b) temperatures precipitation The integrated weather-malaria model(s) malaria season c v (PR <15 ) malaria risk

8. LMM 2010 : annual EIR (EIR a ) and its change (  EIR a ) [infectious mosquito bites per year] 1960-2000 Source: Ermert et al. 2012 EHP, 120, 77-84

9. LMM 2010 & MSM: Changes of the malaria season [month] Source: after Ermert et al. 2012, EHP, 120, 77-84, their Fig. 1C

10. 2021-2030 2041-2050 -1.5 -1 -0.5 -0.1 0.1 0.5 1 2 4 8 LMM 2010 & MSM: Changes of the malaria season [month] Source: Ermert et al. 2012, EHP, 120, 77-84 -6 -4 -3 -2 -1 1 2 3 4 6 Difference plot between the MSM and LMM 2010 (MSM-LMM 2010 ) [month] Source: after Ermert et al. 2012, EHP, 120, 77-84, their Fig. 3C&D Source: after Ermert et al. 2012, submitted to Climate Change

11. → malaria risk S 2005 : Coefficent of variation (c v ) of PR <15 (c v (PR <15 ) ) 1960-2000 = cvcv Source: Ermert (2010), PhD dissertation, University of Cologne, Germany

12. S 2005 : Coefficent of variation (c v ) of PR <15 (c v (PR <15 ) ) = cvcv Source: Ermert (2010), PhD dissertation, University of Cologne, Germany

13. S 2005 : Change of malaria risk c v : coefficient of variation 1960-2000 A1B 2021-2030 2041-2050 Source: Ermert et al. 2012, EHP, 120, 77-84

14. Sahel N today East African Highlands 2050 higher temperatureslower precipitation stable malaria malaria epidemics malaria free Projected future changes of malaria in Africa ~2000 m ~2500 m

15. OUTLOOK Liverpool Malaria Model  Inclusion of some malaria control activities  Estimation of the time window for expected changes of: altitude range of malaria latitudinal change of malaria in the Sahel region  Information especially needed by decision-makers QWeCI  Seamless climate-disease projections in pilot countries (Senegal, Ghana & Malawi) e.g. seasonal malaria forecasts  Health Early Warning System See, for example, Morse et al. 2012 (Poster Z76 EGU2012-1559) The QWeCI Project: seamlessly linking climate science to societyEGU2012-1559 VECTRI (Vector borne disease model of Trieste)  Development of a community malaria model See Tompkins et al. 2012a (Poster Z85 EGU2012-12193)EGU2012-12193 VECTRI: A new dynamical disease model for malaria transmission Tompkins et al. 2012b (Poster Z86 EGU2012-12228) A simple pond parametrization for malaria transmission modelsEGU2012-12228

16. Thank you for your attention! Contact vermert@meteo.uni-koeln.de Peer-reviewed publications Ermert et al. 2011a. Malaria Journal, 10:35. Ermert et al. 2011b. Malaria Journal, 10:62. Ermert et al. 2012a. Environmental Health Perspectives, 120, 77-84. PhD thesis Ermert V. 2010. Risk assessment with regard to the occurrence of malaria in Africa under the influence of observed and projected climate change. University of Cologne. http://kups.ub.uni-koeln.de/volltexte/2010/3109/