Projected impacts of climate change on malaria in Africa

In the twenty-first century, 15 years of declining malaria burden in Africa have been followed by a decade of faltering progress. Growing optimism around new control tools is being tempered by the concurrent challenges of uncertain financing and intensifying biological threats. At this critical juncture, a question of profound importance is the extent to which climate change threatens progress against the disease and the feasibility of eradication as a mid-century goal.

The link between climate and malaria is widely accepted and extensive research has elucidated different causal pathways and sought to project future impacts^1,2. Such research has permeated local and global malaria policy formulation^6,7,8,9, but the ongoing lack of consensus on the directionality, magnitude and geographical distribution of climate change effects hinders detailed response strategies^2,10.

Climate mediates malaria vector and parasite ecology through several mechanisms. Previous studies have explored the effects of ambient temperature on Anopheles mosquito lifespan, blood meal frequency and other life history characteristics^11,12, the duration of Plasmodium extrinsic incubation period¹³, and the combined implications for vectorial capacity and malaria transmission intensity^11,12,13. Recent advances have tailored this understanding to the most important malaria vectors in Africa—members of the Anopheles gambiae complex¹⁴—and the invasive vector Anopheles stephensi¹⁵. Other work has focused on understanding the way climate affects the abundance of adult mosquitoes by influencing larval habitat and its suitability to sustain development of the larval stages^16,17.

As understanding of climate effects on malaria transmission ecology has developed, many studies have extrapolated those mechanisms under future climate scenarios to predict how malaria may respond to climate change³. Some studies have incorporated temperature effects only^18,19, whereas others have used more comprehensive empirical or mechanistic models to capture temperature, rainfall and humidity effects on both larval and adult mosquito stages^4,20,21. Most commonly, these efforts have aimed to predict changes in the geographical (or seasonal) boundaries of transmission^21,22,23. However, many areas of the world are already suitable for malaria transmission yet have none, whereas within existing boundaries transmission can vary by several orders of magnitude. Understanding boundary changes therefore provides only partial insight into climate change impacts on malaria.

Other studies have sought to use mechanistic models to predict climate change impacts on transmission in absolute terms, on the basis of either extrapolated parametrizations from laboratory or field observations^20,21, or calibration to local observations^24,25. However, such models show limited ability to reproduce historical endemicity²⁶ or observed patterns of malaria infection prevalence across Africa, casting doubt on their reliability for projecting future changes in those patterns^17,20,22.

Nearly all existing projections share a central limitation: although they explore climate effects in isolation, they do not adequately account for non-climate determinants of malaria trends. Rising drug resistance in the 1990s contributed to increasing malaria trends in many locations, including those studied intensively for climate change effects^23,27. Subsequently, aggressive scale up of effective interventions between 2000 and 2015 almost halved mean infection prevalence across the continent, albeit unevenly²⁸. This, along with rapid urbanization and socioeconomic development, has reshaped the modern-day landscape of malaria risk, driven long-term changes independently of climate trends and mean that identical climate conditions can host very different levels of malaria transmission^29,30. Failing to account for these factors prevents an accurate assessment of the role of climate—and, by extension, climate change—in shaping future malaria risk.

The built environment and malaria control efforts not only mediate transmission independently of climate but also provide additional pathways by which weather and climate can impact malaria. Extreme weather events such as floods and cyclones damage homes and infrastructure, disrupting access to healthcare, protective housing and malaria control. Malaria surges linked to recent extreme weather events in Africa and Asia have been documented widely^31,32, including causal studies on the impact of disrupted malaria control, even after substantial emergency interventions³¹. In Africa, climate change is predicted to lead to more frequent and severe floods, and more severe southern Indian Ocean cyclones³³. Although some attention has been given to vector-borne disease and extreme weather in localized studies³⁴, previous climate–malaria work has focused almost exclusively on ecological mechanisms. The disruptive effects that intensifying extreme weather events might have on malaria, and vector-borne diseases more broadly³, and the control of such diseases across Africa has not previously been assessed quantitatively.

Here we provide projections of how both ecological and disruptive climate-change effects might affect malaria in Africa. Crucially, we first characterize the climate–malaria relationship in the context of the other key determinants of malaria risk, based on 25 years of comprehensive data on malaria infection prevalence, intervention coverage and socioeconomic conditions. A schematic overview of our analytical framework is shown in Extended Data Fig. 1. Bias-corrected and downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) member global climate model (GCM) outputs obtained from the NASA Earth Exchange Global Daily Downscaled Projections provided future projections of climatic variables to 2050 (ref. ³⁵), and were calibrated to observed climate data^36,37,38 from the co-observed 2000–2014 period, generating consistent time-series of climatic variables from 2000–2050 at monthly time-steps and 5 × 5 km spatial resolution. Between-GCM uncertainty and variability were accounted for using an ensemble of CMIP6 members—14 models for evaluating ecological impacts, and a tractable subset of three for disruptive impacts. We focused analysis on the ‘middle of the road’ scenario SSP 2-4.5, designed to be broadly consistent with current international pledges on reduced greenhouse gas emissions⁵.

Ecologically driven malaria impacts were assessed by first using mechanistic models to transform the processed climate data into two indices representing the climate–malaria interface: (1) the effects of temperature on mosquito and parasite lifecycles and their interaction and (2) the interaction of rainfall, humidity and temperature to determine the relative availability of transient larval habitat for mosquito oviposition and larval development. Use of GCM outputs with monthly resolved projections enabled appropriate propagation of changing regimes of seasonal, inter-annual and inter-decadal climate variation. Unlike previous seasonal models¹⁸ focusing only on temperature effects, this approach allows seasonality to be characterized by the intersection of temporally varying temperature, precipitation and humidity conditions. Second, for the period of observational record in the training data (2000–2022), the two climatic indices were used as geotemporal predictor variables, along with gridded data on mosquito relative species abundance³⁹, permanent larval habitat availability, geotemporal estimates of vector control coverage⁴⁰, seasonal malaria chemoprevention (SMC), antimalarial drug treatment⁴¹ and improved housing⁴² in a hierarchical Bayesian geotemporal model fitted to 49,994 geo-located observations of malaria infection prevalence (Plasmodium falciparum parasite rate (PfPR): age- and diagnostic-standardized parasitaemia rates in 2- to 10-year-old children, henceforth PfPR_2–10) collated across Africa by the Malaria Atlas Project⁴¹. This framework extended an established approach²⁸ and estimated empirically the relative and absolute effects of each predictor variable, including their interactions, on malaria transmission. Third, holding malaria control coverage and socioeconomic metrics at present-day levels, the fitted model was used to generate both SSP 2-4.5 and counterfactual (that is, projecting present-day conditions with no future climate change) PfPR_2–10 scenarios for each climate ensemble member from 2024 to 2050, which were then differenced to generate estimates of climate change impact (see detailed scenario definitions in Extended Data Table 1).

Disruption-driven malaria impacts were assessed by first simulating plausible scenarios of flooding and cyclone events in sub-Saharan Africa consistent with both past observations^43,44 and GCM-projected future climate using statistical models calibrated and validated to historical events. For fluvial and pluvial flooding, the frequency, duration and extent of observed flood events were associated with a range of climate variables to generate a set of geotemporal catalogues of simulated future flood events under both counterfactual (without future climate change) and SSP 2-4.5 conditions to 2050. Similarly, the statistical relationship between climate variables and past Indian Ocean cyclone intensity and track morphology was characterized using methodology and datasets from a recently updated storms model⁴⁵ that was also then projected to generate SSP 2-4.5 and counterfactual event catalogues to 2050.

Although evidence exists on the link between extreme weather events and vector-borne disease^46,47, including malaria, impacts have almost never been measured directly. We assembled both qualitative and quantitative records of past extreme weather events in malaria-endemic settings that detailed the type and magnitude of damage and disruption experienced, and supplemented these with insights from 34 stakeholder interviews, capturing first-hand accounts of malaria impacts on the ground, including from representatives of humanitarian response agencies, national malaria programmes and global health agencies. This mixed-methods approach identified four primary pathways by which extreme weather events can impact malaria: by damage or disruption to the protective effects of (1) improved housing, (2) indoor vector control tools or (3) access to antimalarial treatment, or from changes to Anopheles larval habitat availability due to flood water (Extended Data Table 2). For (1)–(3), we used the compiled evidence (Extended Data Table 3) to define plausible levels of disruption by event type, duration and intensity—distinguishing between acute disruption in the immediate aftermath, and a period of persisting disruption before return to pre-event conditions (see Extended Data Fig. 2 for a schematic overview of our approach). To reflect the inherent variability and uncertainty in these disruption parameters, we also generated ranges spanning 50% to 150% of the central consensus values. A broad uncertainty range was considered appropriate because the scarcity of observational data precluded a more formal quantification of variation in these disruption parameters. These uncertainty ranges were propagated into all downstream modelling steps and the final results. By combining the disruption parameters with the simulated extreme events, we generated modified versions of the geotemporal data layers for vector control coverage, antimalarial drug treatment and improved housing that incorporated disruptions under both SSP 2-4.5 and counterfactual scenarios to 2050. Larval habitat impacts were captured by the habitat suitability model described earlier. The fitted hierarchical Bayesian geotemporal model was then used to reproject PfPR