Predicting malaria risk considering vector control interventions under climate change scenarios

Abstract

Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010–2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min–Max]: 16,785 [9,902–74,382] vs 21,289 [11,796–70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa

“…However, the current model tuning does not consider the introduction of various vector control interventions (such as bednet use), leading to an earlier onset of malaria season as shown in Figure 11. The decline in both clinical episodes and deaths due to malaria is primarily associated with the expansion of vector-control measures, such as insecticide-treated bednets and indoor residual spraying (Elmardi et al, 2021;Musiime et al, 2019;Sadoine et al, 2024). While tuning model parameters, we observed a significant sensitivity of VECTRI schemes S1 and S2 to the parameter "wperm" (fraction of permanent water bodies, see Equation 4), that is, setting "wperm" to 0.001 produces results that match the observed malaria cases from January to May.…”