The Response of Environmental Capacity for Malaria Transmission in West Africa to Climate Change

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1 AGU Fall Meeting December 9, 2011 The Response of Environmental Capacity for Malaria Transmission in West Africa to Climate Change Teresa K. Yamana & Elfatih A.B. Eltahir MIT Dept. of Civil & Environmental Engineering

2 Research Question How will environmental suitability for malaria transmission in West Africa respond to climate change scenarios predicted by current GCMs? In some scenarios, the effects of warming and changing precipitation act in opposite directions, so the overall effect of malaria transmission is unknown.

3 RELATIONSHIP BETWEEN CLIMATE AND MALARIA

4 Anopheles gambiae mosquito ecology human infected by mosquito intrinsic parasite incubation 1st bloodmeal: acquire infection 2nd bloodmeal: transmit infection extrinsic parasite incubation adult oviposition pupa egg L4 larva L1 larva L3 larva L2 larva Bomblies, 2008

5 Anopheles mosquito ecology human infected by mosquito intrinsic parasite incubation 1st bloodmeal: acquire infection 2nd bloodmeal: transmit infection Adult longevity = f(t) adult extrinsic parasite incubation oviposition Temperaturedependent rates pupa egg L4 larva L1 larva L3 larva L2 larva Bomblies, 2008

6 Timescales of mosquito lifespan and malaria development EIP too long: no transmission Ideal for transmission Too hot for mosquito

7 Measure of climate suitability: Vectorial Capacity Vectorial Capacity: Number of inoculations from a single infected person per day VC ma 2 p n d m: mosquitoes per human a: bites per mosquito per day p: probability mosquito survives one day n: extrinsic incubation period d: average number of days until mosquito dies p,n and d depend on temperature m and a depend on temperature and rainfall

8 Measure of climate suitability: Vectorial Capacity Vectorial Capacity: Number of inoculations from a single infected person per day VC ma 2 p n d Temperature dependent equations m: mosquitoes per human a: bites per mosquito per day p: probability mosquito survives one day n: extrinsic incubation period d: average number of days until mosquito dies p,n and d depend on temperature m and a depend on temperature and rainfall

9 Measure of climate suitability: Vectorial Capacity Vectorial Capacity: Number of inoculations from a single infected person per day VC ma 2 p n d Temperature dependent equations Need Model m: mosquitoes per human a: bites per mosquito per day p: probability mosquito survives one day n: extrinsic incubation period d: average number of days until mosquito dies p,n and d depend on temperature m and a depend on temperature and rainfall

10 CURRENT CLIMATE IN WEST AFRICA

11 North-South Gradients in Temperature and Rainfall

12 North-South Gradients in Temperature and Rainfall North: Already hotter than ideal -> Warming decreases VC ideal temperature for malaria transmission VC ma 2 p n d South: Cooler than ideal -> Warming increases VC

13 Estimated malaria prevalence 18W,4N

14 PREDICTED CLIMATE IN WEST AFRICA

15 Change in climate predicted by IPCC Assessment Report 4 A1B emissions scenario

16 Expected effect of Climate Change Warming Current Temperature > 29C (North) < 29C (South) Change in Rainfall Less Rain More Rain Less Rain More Rain Overall Effect on Vectorial Capacity Decreased VC?? Increased VC Red: Higher Vectorial Capacity Green: Lower Vectorial Capacity Orange: Unknown

17 Changes predicted by IPCC models Box 1 Change in climate predicted by a GCM

18 Changes predicted by IPCC models Box 1 Maximum transmission Minimum transmission Change predicted by GCM

19 Changes predicted by IPCC models Box 1 Box 2 Box3 Maximum transmission Minimum transmission Change predicted by GCM

20 HYDREMATS: Hydrology Entomology & Malaria Transmission Simulator human infected by mosquito intrinsic parasite incubation 1st bloodmeal: acquire infection 2nd bloodmeal: transmit infection extrinsic parasite incubation adult oviposition pup a eg g L4 larva L1 larva L3 larva L2 larva Overland flow model will pool water and simulate pool losses to infiltration/evaporation Bomblies et al. Water Resources Research, 2008

21 PRELIMINARY RESULTS FROM HYDREMATS

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25 CONCLUSIONS

26 Conclusions Even under the worst case scenario, we do not expect to see a major increase of malaria transmission in this region Box 1: Higher VC, but still too low for transmission Box 2: Increase in VC due to increased rainfall balances the decrease in VC due to higher temperature Box 3: Small increase in VC over the 9 years The hottest and driest scenarios would eliminate transmission in Boxes 1 & 2, and substantially decrease transmission in Box 3

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28 EXTRA SLIDES

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30 Range of predicted changes in temperature Box CRU : temperature in rainy months Max rainy Model season increase predicting max increase Min rainy Model season increase predicting min increase GFDL/NOAA 2.3 NCAR - CCSM ECHAM 2.6 NCAR - CCSM University of Tokyo MIROC high-res 2.8 NCAR - CCSM University of Tokyo MIROC NASA/GISS - AOM high-res 2.6 University of Tokyo MIROC high-res 2.3 CSMK3

31 Range of predicted changes in rainfall Box CRU Max increase wettest Max decrease driest NCAR -105 GFDL/NOAA NCAR -206 GFDL/NOAA ECHAM + HOPEG -254 GFDL/NOAA ECHAM + HOPEG -212 GFDL/NOAA University of Tokyo MIROC NASA/GISS E-H -227 med-res

32 Temperature increases Current Temp > 29C (North) Current Temp <29 C More Rain Less Rain More Rain Less Rain? Expect Decrease Expect Increase?

33 Change in Vectorial Capacity due to temperature alone 0% -10% -20% -30% -40% -50% -60% -70% -80% -90% -100% Chart Title Warm Wet Hot Dry VC ma Warm Wet: Maximum increase in precipitation and minimum increase in temperature Worst Case Scenario Hot Dry: Minimum Increase in precipitation and maximum increase in temperature Best Case Scenario 2 p n d