Solar geoengineering could redistribute malaria risk in developing countries

Climate change significantly threatens human health, particularly in developing countries which already face severe impacts and adaptation challenges. Existing greenhouse gas emission mitigation pledges are insufficient to prevent global warming exceeding 2°C, prompting consideration of radical responses like solar geoengineering (SRM), including stratospheric aerosol injection (SAI). SAI aims to offset warming by reflecting more sunlight back into space. While SAI's effects on temperature and precipitation are relatively well-studied, its consequences for human health and ecosystems remain largely unknown. Although global warming's negative health impacts are well-established, SRM would still create a climate differing from current or pre-industrial climates, making uniform health improvements uncertain. Existing research has touched upon SAI's potential impact on skin cancer and pollution-related illnesses, but the implications for infectious diseases, a major cause of global mortality (especially in low- and middle-income countries), are poorly understood. This lack of knowledge constitutes a critical research gap, especially considering the growing interest in solar geoengineering and the need for comprehensive health risk assessments in climate change policy-making. Vector-borne diseases, with their substantial global burden and demonstrated climate linkages, are ideal candidates for such assessments. Arthropod-borne diseases are highly sensitive to temperature, influencing both pathogen replication rates and vector activity. Their unimodal response to temperature allows for modeling of transmission windows and basic reproduction numbers (R0), enabling projections under varying climate scenarios and the assessment of epidemic or endemic transmission potential. Existing evidence suggests a strong link between climate change and the resurgence of vector-borne diseases. For instance, by 2070, climate change is expected to increase the global population at risk of dengue fever by up to one billion, with similar projections for Zika virus. Malaria, however, presents an exceptionally high priority for risk assessment in geoengineering research due to its significant global burden and its unusual adaptation to cooler temperatures. The current study directly addresses the potential impacts of SAI on malaria transmission.

A substantial body of literature highlights the significant threat of climate change to global health, particularly concerning infectious diseases. Studies have extensively documented the climate sensitivity of vector-borne illnesses, emphasizing the impact of temperature on pathogen replication and vector activity. This has led to the development of sophisticated models projecting changes in disease transmission under varying climate scenarios, including projections for dengue, Zika, and yellow fever. Concerning malaria, the literature indicates a likely expansion of *P. falciparum* malaria into southern Africa and high-elevation regions of East Africa, potentially reducing transmission in central Africa and the Sahel. However, comparatively less attention has been paid to *P. vivax*, despite its significant global prevalence and potential for resurgence. The recent resurgence of malaria in Latin America, linked to political instability and migration, underscores the importance of considering the interplay of social and environmental factors. While the existing literature provides valuable insights into climate change's impact on malaria, a critical gap exists in understanding the potential consequences of solar geoengineering interventions.

This study investigates future malaria risk under scenarios with and without stratospheric aerosol injection (SAI) to mitigate climate change. Two GeoMIP scenarios were utilized: G3, injecting SO2 aerosols from the equator to offset warming from RCP 4.5; and GLENS, injecting aerosols at four locations to offset warming from RCP 8.5. Three ensemble members from the HadGEM2-ES model (G3, RCP 4.5) and CESM1 (WACCM) model (GLENS, RCP 8.5) were used to account for climate system variability. These scenarios were chosen for their realistic simulation of stratospheric aerosol layers and their differing injection strategies. Other scenarios were excluded due to potential confounding factors. The analysis focused on Africa, Asia, and Latin America, regions currently endemic for malaria. Higher latitude regions were excluded due to strong healthcare systems likely preventing malaria establishment. The study employed a temperature-dependent basic reproduction number (R0(T)) approach, which serves as a proxy for transmission suitability but does not predict total incidence. This approach, which has been used to assess risk for other diseases, identifies areas where transmission intensity might be higher or lower under different scenarios. The R0(T) values, derived from a life-history explicit transmission model, were mapped to identify potential changes in transmission suitability. The analysis compared transmission suitability in 2070 with and without SRM, and compared 2070 suitability with that of 2020. Outer bounds of transmission (Tmin and Tmax) were identified to assess the potential changes in transmission seasonality. Finally, populations at risk were projected using population data from SEDAC, pairing RCPs with appropriate SSPs to estimate population distributions in 2070. The study also considered the impacts of malaria control efforts by visually comparing recent prevalence declines with R0(T) values, acknowledging that model limitations prevented direct incorporation of interventions.

The study revealed that high-intensity malaria transmission will remain concentrated in sub-Saharan Africa and the Indian subcontinent. However, significant differences in transmission suitability emerged between scenarios, particularly pronounced with geoengineering to offset RCP 8.5. Compared to climate change alone, SRM might substantially reduce malaria transmission in the Indian subcontinent and Sahel, and in high-elevation areas. However, SRM could increase transmission suitability in tropical regions like the Amazon basin, Indonesia, and parts of Africa, primarily by stabilizing temperatures at cooler levels. Comparing 2070 geoengineering scenarios to 2020 showed that G3 led to higher suitability in much of Africa and Southeast Asia but reductions in Latin America, while GLENS resulted in lower suitability across the tropics compared to 2020. Analyses of transmission seasonality showed that changes in transmission seasonality were relatively consistent across models for each scenario by 2100, except for Southern Asia which showed high variability. Overall, the analysis indicated that geoengineering strategies, while potentially offering benefits in some areas, could also lead to considerable increases in malaria risk in other regions relative to climate change without geoengineering. This emphasizes that geoengineering does not guarantee uniform health improvements and may create regional trade-offs.

The findings demonstrate the potential for unintended consequences of solar geoengineering on malaria distribution and prevalence. While some regions might experience reduced malaria risk, others could see significant increases, negating the projected reduction of nearly one billion people at risk of malaria by 2070. The study highlights the complex non-linear relationships between temperature and infectious disease dynamics, emphasizing the need for region-specific impact assessments rather than relying on general assumptions about geoengineering's health benefits. The results underscore the critical need for involving developing countries in geoengineering discussions, recognizing the potential for uneven distribution of risks and benefits. The current emphasis on potential disease incursions into wealthier, higher-latitude countries overshadows the disproportionate burden of infectious diseases on poorer, tropical countries.

This study reveals the potential for solar geoengineering to redistribute malaria risk, highlighting the importance of region-specific assessments. The results demonstrate the limitations of generalized assumptions about geoengineering's health impacts and underscore the necessity for involving developing countries in decision-making processes. Future research should focus on refining models to incorporate human interventions and other climate variables and consider the wider context of planetary health scenarios, including the potential impacts of weakened healthcare systems following crises like the COVID-19 pandemic.

This study used a simplified approach to estimate population at risk, focusing primarily on temperature as a factor in malaria transmission. While the R0(T) model is a valuable tool, it doesn't incorporate the complexity of real-world transmission dynamics, including human behavior, vector control measures, or other environmental factors. The choice of climate models and scenarios also introduces an unquantified layer of uncertainty. The lack of direct incorporation of healthcare interventions limits the ability to precisely predict disease burden in regions with robust control programs. Future research should address these limitations through more sophisticated models and data integration.