Multiple timescale climate drivers of malaria: Counterfactual ensembles for climate attribution in health from the ACCLIMATISE Project

Assessing the efficacy of malaria interventions is increasingly complicated by a changing climate, which can mask or mimic the impacts of public health policies. To robustly attribute changes in disease burden, it is essential to isolate the non-linear impacts of climate trends and variability from intervention effects.

This study introduces the scientific framework of the ACCLIMATISE project (funded by the Wellcome Trust in the ATTRIVERSE program) utilizing the VECTRI dynamical malaria model to simulate transmission under a range of climate counterfactuals. Using latrge ensembles, Our approach filters driving temperature and precipitation data to selectively remove specific modes of variability—ranging from climate change through decadal and multi-year cycles to interannual variability. This experimental setup allows us to disentangle the distinct roles of warming and hydrological variability in driving transmission dynamics across Africa.

We present preliminary results demonstrating how these filtered climate drivers alter simulated malaria baselines, highlighting the sensitivity of the model to specific timescales of climate forcing through temperature and rainfall separately as well as their nonlinear interaction. These simulations establish a "climate-only" reference frame. The ACCLIMATISE project will confront these counterfactual baselines with health observations to attribute the role of climate in this health outcome and separate the signal of malaria interventions from the influence of climate variability and change.