The effect of explicit convection on simulated malaria transmission across Africa

• Published in: PloS one 2024-04, Vol.19 (4), p.e0297744-e0297744
• Main Authors: Talib, Joshua, Abatan, Abayomi A, HoekSpaans, Remy, Yamba, Edmund I, Egbebiyi, Temitope S, Caminade, Cyril, Jones, Anne, Birch, Cathryn E, Olagbegi, Oladapo M, Morse, Andrew P
• Format: Article
• Language: English
• Subjects: Africa - epidemiology; Analysis; Biology and Life Sciences; Climate models; Computer Simulation; Convection; Disease transmission; Earth Sciences; Global temperature changes; Humans; Hydrology; Hydrology - methods; Malaria; Malaria - epidemiology; Medicine and Health Sciences; Methods; People and Places; Physical Sciences; Prevention; Rain and rainfall; Research and Analysis Methods; Risk factors; Seasonal variations (Diseases); Simulation methods
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0297744

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.