A spatial agent-based model of Anopheles vagus for malaria epidemiology: examining the impact of vector control interventions

Background: Malaria, being a mosquito-borne infectious disease, is still one of the most devastating global health issues. The malaria vector Anopheles vagus is widely distributed in Asia and a dominant vector in Bandarban, Bangladesh. However, despite its wide distribution, no agent based model (ABM) of An. vagus has yet been developed. Additionally, its response to combined vector control interventions has not been examined. Methods: A spatial ABM, denoted as ABMvagus, was designed and implemented based on the biological attributes of An. vagus by modifying an established, existing ABM of Anopheles gambiae. Environmental factors such as temperature and rainfall were incorporated into ABMvagus using daily weather profiles. Real-life field data of Bandarban were used to generate landscapes which were used in the simulations. ABMvagus was verified and validated using several standard techniques and against real-life field data. Using artificial landscapes, the individual and combined efficacies of existing vector control interventions are modeled, applied, and examined. Results: Simulated female abundance curves generated by ABMvagus closely follow the patterns observed in the field. Due to the use of daily temperature and rainfall data, ABMvagus was able to generate seasonal patterns for a particular area. When two interventions were applied with parameters set to mid-ranges, ITNs/LLINs with IRS produced better results compared to the other cases. Moreover, any intervention combined with ITNs/LLINs yielded better results. Not surprisingly, three interventions applied in combination generate best results compared to any two interventions applied in combination. Conclusions: Output of ABMvagus showed high sensitivity to real-life field data of the environmental factors and the landscape of a particular area. Hence, it is recommended to use the model for a given area in connection to its local field data. For applying combined interventions, three interventions altogether are highly recommended whenever possible. It is also suggested that ITNs/LLINs with IRS can be applied when three interventions are not available.