Vulnerability to changes in malaria transmission due to climate change in West Africa
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1 AGU Fall Mee0ng December 4, 2012 Vulnerability to changes in malaria transmission due to climate change in West Africa Teresa K. Yamana & Elfa0h A.B. Eltahir MIT Dept. of Civil & Environmental Engineering
2 Research Ques0ons Which areas in West Africa are most sensi0ve to changes in malaria transmission due to climate change? What changes do we expect to see in these areas?
3 RELATIONSHIP BETWEEN CLIMATE AND MALARIA
4 Anopheles 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 Measure of climate suitability: Vectorial Capacity Vectorial Capacity: Number of inoculagons from a single infected person per day VC = ma 2 n p ln( p) m: mosquitoes per human a: bites per mosquito per day p: probability mosquito survives one day n: extrinsic incuba0on period 1/-ln(p): average number of days un0l mosquito dies p and n depend on temperature m and a depend on temperature and rainfall
7 Change in Prevalence due to Climate change PR PR VC = climate VC climate PR: Prevalence - Propor0on of infected individuals in the popula0on
8 RELATING VECTORIAL CAPACITY TO MALARIA PREVALENCE
9 Malaria Prevalence in West Africa Gething, P.W.* et al. (2011). A new world malaria map: Plasmodium falciparum endemicity in Malaria Journal, 10: 378.
10 Rela0onships between PR, EIR, VC Entomological InoculaGon Rate (EIR): Infec0ous bites per person per year Smith et al. (2007). PLoS Biology
11 Rela0onships between PR, EIR, VC bαeir PR = r 1+ SK VC = EIR K 1+ K = c(1 (1 PR) α ) 1/ α Smith et al. (2007). PLoS Biology EIR: Infec?ous bites per person per year S=a/-ln(p): bites per mosquito life0me a: bites per mosquito per day p: probability mosquito survives one day b: mosquito to human transmission efficiency c: human to mosquito transmission efficiency α: accounts for heterogeneity in human biting rate
12 Vectorial Capacity and Prevalence For effects of climate change, most interested in rela0onship between VC and prevalence VC = r [(1 bα PR) 1] α 1 + SK K VC critical = r bc (1 +α)
13 Es0mated Vectorial Capacity
14 Deriva0ve of Prevalence w.r.t. VC Numerically differen0ate PR PR VC VC = climate VC VC climate
15 Deriva0ve of Prevalence w.r.t. VC Most Sensi0ve Medium Sensi0vity Least Sensi0ve
16 Sensi0vity to Vectorial Capacity
17 Sensi0vity to Vectorial Capacity????
18 CHANGE IN VECTORIAL CAPACITY DUE TO CLIMATE CHANGE
19 Simula0ng Vectorial Capacity
20 Simula0ng Vectorial Capacity
21 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
22 Climate Projec0ons rainfall (mm) Mean Annual Rainfall future current Temperature (Celsius) Mean Wet Season Temperature High 1 High 2 Medium 3 Medium 4 0 High 1 High 2 Medium 3 Medium 4
23 Percent change in simulated VC High 1 40% High 2-66% Medium 3-35% Medium 4 95% PR PR VC VC = climate VC VC climate
24 Change in Prevalence assuming wegest future climate PR VC VC VC VC climate PR climate New P High % High % Medium % Medium % Prevalence Current Future H1 H2 M3 M4
25 Conclusions Malaria prevalence is most sensi0ve to changes in vectorial capacity in areas along the northern boundary of current malaria areas, where transmission is currently low The areas where we expect to see the greatest increases in VC are not necessarily the areas where prevalence is most sensi0ve to changes in VC
26 Contact informa0on eltahir.mit.edu
27 EXTRA SLIDES
28
29 Range of predicted changes in temperature Box CRU : Max rainy Model temperature in season increase predicgng max rainy months increase Min rainy Model season increase predicgng 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
30 Range of predicted changes in rainfall Box CRU Max increase wewest 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
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