Last Updated: 17/12/2025
Data-driven models to assess impacts of integrated vector management strategies on mosquito-borne diseases.
Objectives
This project aims to adapt and extend customizable bioinformatics systems for collating and standardizing entomological and environmental datasets, enabling multiple researchers to more effectively collaborate, share, and synthesise data using standardized formats across multiple studies and study sites.
Improvement and optimization of existing mosquito control interventions and introduction of complementary new and effective tools is essential, particularly in areas of high disease burden, for reducing transmission of mosquito-borne infection to zero. Mathematical models can play an essential role in evaluating these interventions, but need high quality data, either for parameterization or as input values to predict outcomes. Unfortunately, in vector control, adequately or uniformly collated m osquito data in standardisable formats is rarely available to facilitate integrated analyses and interpretation. This project will demonstrate, using the high-quality data collected in this system, that highly sensitive data-driven mathematical models can be developed to evaluate impacts and cost-effectiveness of new and combined interventions against the three main malaria vectors in sub-Saharan Africa: Anopheles gambiae s.s, An. arabensis, and An. funestus. The models will allow a priori evaluation of existing and new interventions on naive or residual vector populations, and will be expandable to other mosquito-borne diseases.
Jan 2016 — Dec 2018
$280,095
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