Modelling the impact of climate variability on the effectiveness of seasonal indoor residual spraying

Malaria transmission is strongly modulated by climate, yet most mechanistic models used for health policy evaluation do not explicitly account for climate-driven variability in mosquito dynamics. Here, we present a novel modelling framework that couples VECTRI, a climate-sensitive model for malaria, with OpenMalaria, a detailed individual-based model of malaria epidemiology and intervention impact. This integrated approach enables simulation of transmission processes that respond to interannual climate fluctuations while retaining the capacity to evaluate realistic intervention strategies. We apply this framework to Mozambique, a high-burden country for malaria, with pronounced climatic seasonality and extensive routine surveillance data. Using district-level incidence time series from the ten highest-incidence districts, we validate the modelling framework and compare its performance with the standalone versions of VECTRI and OpenMalaria. The joint modelling framework reproduces malaria seasonality more accurately than the two single models, with improved timing of incidence peaks in 7 out of 10 districts and a closer representation of evolution of transmission throughout the season, as measured by the seasonality index, in 7 out of 10 districts. We then use the coupled system to assess how climate-driven interannual variability affects the predicted effectiveness of indoor residual spraying, a key seasonal malaria control intervention in Mozambique. Integrating climate-driven interannual variability into OpenMalaria substantially impacts the modeled effectiveness of indoor residual spraying. The joint modelling framework increases the estimated protective effectiveness of indoor residual spraying in reducing incidence by up to 11% and delayed the optimal deployment window by two weeks. Our results demonstrate that climate-informed mechanistic models can meaningfully alter estimates of intervention impact and improve the realism of malaria predictions. The joint OpenMalaria-VECTRI modelling framework provides a flexible tool for national malaria programs seeking to evaluate seasonal interventions under varying climatic scenarios. Author summaryMalaria transmission in Mozambique changes from year to year because mosquito populations strongly depend on rainfall and temperature. However, most models used to guide malaria control planning do not fully capture these climate-driven fluctuations. We developed a new modelling approach that links two existing tools: VECTRI, which simulates climate-sensitive mosquito and malaria dynamics, and OpenMalaria, which simulates malaria infections and the effects of interventions. We validated this joint modelling framework using routine surveillance malaria data from Mozambique and found that it better captures observed seasonal and interannual patterns than standalone models. We then used it to explore how climate variability influences the effectiveness of indoor residual spraying, an important seasonal malaria control strategy. Accounting for climate-driven mosquito dynamics reduced the predicted protective effectiveness of indoor residual spraying by 11% and shifted the optimal deployment timing by approximately two weeks. These results show that integrating climate information into malaria models can improve the accuracy and usefulness of intervention planning.