Quantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI)
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1 Quantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI) QWeCI is funded by the European Commission s Seventh Framework Research Programme under the grant agreement partners from 9 countries Impact of climate change on malaria transmission in Africa: the good, the bad and the unknown Caminade Cyril School of Environmental Sciences Institue of Infection & Global Health. University of Liverpool Cyril.Caminade@liverpool.ac.uk
2 Introduction Malaria Background Caused by Plasmodium spp parasite which is transmitted by bites of the Anopheles spp mosquito WHO Global elimination program in mid-20 th C was successful in Europe and USA Now mainly sub-saharan Africa (P. Falciparum) (91%) and Asia (P. Falciparum and P. Vivax) Mainly affects children, pregnant women and elders Estimated 660,000 deaths worldwide in 2010 Fallen 33% in sub-saharan Africa since > Roll Back Malaria Programme, Bill & Melinda Gates fundation, World Bank Malaria Booster programme...
3 Malaria Modelling A bit of History Sir Ronald Ross, Nobel Prize 1902 working in Liverpool, created the first malaria model Ross published his theory of A priori Pathometry mathematical approach to study of disease dynamics general approach (not just malaria) -> First Ro Model formulation In contrast to the a posteriori method fitting analytics to observed data (i.e. statistical modelling). Subsequently refined and extended (Lotka, 1923, Macdonald, 1957, Dietz, 1975, Aron and May 1982, Smith and McKenzie, 2004, see Smith et al., 2012 for a review ) First multi-malaria model inter-comparison exercice -> ISI-MIP / QWeCI / Healthy Futures
4 Methodology ISI-MIP framework ISI-MIP Inter-Sectoral Impact Model Intercomparison. Aim: Using an ensemble of climate model simulations, scenarios and an ensemble of impact models to assess simulated future impact changes and the related uncertainties. Five malaria models investigated: MARA, LMM_ro, Vectri, Umea & MIASMA Output Variables: Length of the malaria transmission season e.g. LTS (in months) Malaria climatic suitability (binary 0-1). Defined if LTS >=3 months Additional person/month at risk for the future. Bias corrected climate scenarios were available for all RCPs [2.6, 4.5, 6, 8] and the historical simulations for 5 GCMs. Population scenario SSP2 (UN) GCM1 - HadGem2-ES GCM2 - IPSL-CM5A-LR GCM3 - MIROC-ESM-CHEM GCM4 - GFDL-ESM2M GCM5 - NorESM1-M
5 Methodology Outline Do the malaria models reproduce the observed trends in malaria endemicity over the 20 th century? What is the impact of climate change on malaria distribution based on a multi-model ensemble? Uncertainties? How do the different malaria models behave under a warmer climate? Conclusions
6 20 th century trends over the globe: Simulation versus Observation 1900s 2000s Changes (Δ) obs mara lmm_ro miasma Pre-intervention (1900s): miasma is doing a good job Post-intervention (2000s): mara provides a realistic picture at global scale The observed decline of malaria endemicity over the 20 th century at global scale more related to control measure than climate change
7 obs mara lmm_ro 1900s 20 th century trends over Africa: Simulation versus Observation 2000s Changes (Δ) Pre-intervention (1900s): miasma is doing a good job Post-intervention (2000s): mara provides a realistic picture over Africa Decrease in malaria endemicity over the Sahel reproduced by the malaria models driven by climate parameters only miasma Climate may have partly contributed to the observed changes over the Sahel and the highlands (no causality!)
8 Impact of Climate Change on the length of the malaria transmission season The effect of climate scenarios on future malaria distribution: changes in length of the malaria season (ISI-MIP ensemble). Each map shows the results for a different emissions scenario (RCP). The different hues represent changes in the length of the transmission season for the mean of CMIP5 sub-ensemble (with respect to the historical mean). The different saturations represent signal-to-noise (μ/sigma) across the super ensemble (noise is defined as one standard deviation within the multi-gcm and multi-malaria model ensemble). The stippled area shows the multi-malaria multi GCM agreement (60% of the models agree on the sign of changes if the simulated absolute changes are above one month of malaria transmission). Increase in transmission over the highlands of Africa (east Africa, Madagascar, Angola, southern Africa) / decrease over the Sahel (extreme scenario / long term)
9 Various sources of uncertainties: MIM vs GCMs vs RCPs Largest uncertainties related to the method (malaria model) Uncertainties related to the GCMs huge over the northern epidemic fringe (consistent with the spread in GCM rainfall projections over this region). Emission Scenario uncertainties increase as a function of time (about 40% over the highlands in 2080s, consistent with a Temperature effect on malaria transmission for those regions).
10 Malaria Models Sensitivity Sahel Sahel Sensitivity of the simulated changes in the length of the malaria transmission season (LTS) to mean annual rainfall and temperature for the ISI-MIP ensemble over Africa. Mean changes in the length of the transmission season are calculated for all emission scenarios, GCMs and time slices. If the simulated absolute changes are above one month, then they are plotted versus mean future annual rainfall (mm Y axis) and temperature ( C X axis). Results are similar for the CORDEX ensemble (not shown). Mara, miasma: General Increase in LTS Sahel Sahel lmm_ro, vectri: Increase in the moderate temperature range, decrease for high temperatures (mosquito survival schemes)
11 Conclusion 20 th century trends (globe): Decline in malaria endemicity observed over the 20 th century at global scale not reproduced by malaria models driven by climate drivers only (Intervention!). 20 th century trends (Africa): Climate induced risk consistent over the Sahel and the highlands. Climate change: Consistent increase in malaria transmission over the highlands of Africa Southward shift of the epidemic belt / decrease in LTS over the Sahel for vectri & lmm_ro Population at risk generally increases over Africa (population growth driven, not shown) Largest uncertainties in the future are related to the malaria models RCMs results consistent with GCMs ones (not shown)
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