ArticleScientific reports2026
Modelling the temporal dynamics of Microsporidia MB prevalence in Anopheles mosquitoes under environmental variability.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Releasing Microsporidia MB-positive mosquitoes is being investigated as a promising component of Integrated Vector Management (IVM) strategies for malaria control. In this study, we developed a System Dynamics model to replicate the observed interactions between humans, mosquitoes, the Plasmodium parasite, and the symbiont MB. The model accounts for the impact of environmental variables - such as humidity, rainfall, and temperature - on mosquitoes, Plasmodium, and MB, using exogenous inputs derived from daily climatic observations (rainfall, relative humidity, and temperature) recorded during the mosquito field surveys. We calibrate the model using data collected from Ahero in Kenya, available for 3 years in the past. Model outputs were compared with historical data for two Key Performance Indicators, KPIs: adult mosquito abundance and MB prevalence in adult mosquitoes. Statistics of fit indicate that the model has good ability to represent the trend for the selected KPIs, but not the short-term variation, which is consistent with the objectives of the modelling. Our sensitivity analysis revealed that the equilibrium prevalence of MB-positive mosquitoes is more sensitive to the vertical transmission rate, followed by the male-to-female horizontal transmission rate, while the female-to-male transmission rate has less impact. Additionally, we demonstrated that the distribution of the MB-intensity within the MB-positive mosquito population can drive the extinction or persistence of MB-positive mosquitoes. Finally, we evaluated how variations in key climatic drivers (temperature, humidity, and rainfall) influence the prevalence and spread of Microsporidia MB in adult mosquito populations using sensitivity analyses of climatic datasets. This study is meant for the design of a decision support tool for adoption by policymakers and implementation of an MB-based malaria control intervention.
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