Associated organisationsThe Abdus Salam International Centre for Theoretical Physics (ICTP) · Gaston Berger University · Kenya Medical Research Institute (KEMRI) · New York University · Swiss Tropical and Public Health Institute · Universite Cheikh Anta Diop de Dakar · University of TrentoEurope PMC affiliations are not treated as award recipients or mapped locations.
Funding£3.0M
PeriodJun 2025 — Jun 2028
In plain English
AI plain-English summary
Malaria interventions are being evaluated without properly accounting for the fact that the climate has been changing around them. Warmer temperatures and shifting rainfall patterns alter how the malaria parasite and its mosquito vectors behave, meaning the same intervention can work differently depending on the year or location. Current assessments cannot tell whether a drop in malaria cases is due to a successful bed-net campaign or simply a cooler, drier season. ACCLIMATISE will build computer models that simulate what would have happened to malaria transmission without human-caused climate change, then compare those counterfactual scenarios with real-world data. The team will also develop an AI tool, co-designed with public health officials, to test which combinations of interventions are most cost-effective under different climate conditions out to 2050. If the research succeeds, funders and governments will be able to allocate resources more efficiently—for example, knowing whether to invest in highland outbreak surveillance or in monsoon-season stockpiles of antimalarial drugs. The work is applied, not fundamental science; its direct purpose is to stop money being wasted on interventions that climate change has already rendered less effective.
View original technical description
Recent significant investments in malaria interventions have reduced malaria burden. Contemporaneously, the climate has continued to warm with associated changes in rainfall and weather extremes, which compound year to year and decadal climate variability. Together these impact malaria transmission through the climate sensitivity of the parasite and its mosquito vector. Hence, climate change can either enhance or offset the impact of malaria interventions. Conversely, interventions can be optimised to mitigate the effects of climate, for instance to prevent outbreaks at higher altitudes. Ignoring the confounding effects of climate can result in the impact of interventions being over or underestimated. Equally, neglecting the impact of interventions would render attribution of malaria outbreaks to anthropogenic climate inaccurate. ACCLIMATISE will combine modelling and observations to differentiate the climate signal from that of interventions and other environmental and socioeconomic changes, so that a cost-benefit analysis can fairly assess the efficiency of interventions. We use counterfactual simulations to attribute anthropogenic climate change in malaria trends in endemic settings, as well as extremes in highland areas and the monsoon fringe with a range of intervention scenarios. An AI-emulator will be co-designed with stakeholders to assess efficient intervention strategies in the present and near future to 2050.
Attriverse: Developing Digital Solutions for Health Impact Attribution
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