| Sumario: | Effective control of malaria vectors is crucial for achieving successful malaria elimination. Modelling techniques play a key role in understanding the behaviour and distribution of Anopheles mosquito, the vector responsible for malaria transmission upon infection with the Plasmodium parasite. Despite advances in species distribution
modelling (SDM), the behavioural dynamics of malaria vectors, particularly biting probability, have not been fully integrated into these models. This study aimed to model both the habitat suitability and biting probability of malaria vectors. Specifically, relevant remotely sensed data, climatic and topographic variables, and presenceonly malaria vector data were integrated into MaxEnt (maximum entropy), an SDM to assess the distribution of malaria vectors. Subsequently, additional variables such as the modelled malaria vector presence, human availability, confirmed insecticide resistance and bed net usage were incorporated in fuzzy logic rule-based techniques to assess the spatial and temporal biting risk probability of these vectors from 2000 to 2018. Two different rule-based model scenarios with distinct rule combinations (Model 1: flexible optimal climatic and environmental conditions (i.e., ORs) and Model 2: strict optimal climatic and environmental conditions (i.e., ANDs)) were evaluated. An independent set of validation data with An. gambiae complex biting observations (n =25) for the years (2017-2021) was used. Validation of the models yielded a mean accuracy of 91% for both models. The models showed reduced biting probability with increased bed net usage amid optimal conditions for biting. This pattern was also comparable to the reduced Plasmodium falciparum prevalence rate from 2000 to
2018 due to an increase in intervention measures. The results highlight the significance of integrating modelled malaria vector presence, ecological conditions, human availability/presence and control methods in the assessment of malaria transmission risk as an early warning system in changes to climate and control methods usage. These findings are pivotal for optimizing targeted malaria vector management and malaria elimination strategies, providing essential insights for public health and disease control stakeholders.
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