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Last Updated: 08/11/2024
Using novel technologies for real-time monitoring of the risk of mosquito- borne diseases in the context of climate and land-use changes
Objectives
By combining capabilities in vector-biology, spatial analytics, machine learning and mathematical modeling, This project will:
- Integrate environmental and entomological data-streams to predict transmission risk,
- Investigate climate-dependent survival strategies of medically-important mosquitoes and
- Evaluate entomological data for monitoring climate and land-use.
Transmission of mosquito-borne diseases can be affected by climate and land- use patterns in different ways. Increased temperatures can shorten the latency of malaria parasites thereby enabling transmission in previously-temperate zones. Yet extended droughts could crash populations of vectors such as Anopheles gambiae, which breed in small, open and drought-sensitive habitats. In contrast, flooding can increase populations of container-breeding dengue vectors, Aedes aegypti. Most investigations of climate and vector-borne diseases are too expansive to be computationally-practical; and often overlook local data on entomological, anthropological or land-use characteristics. Fortunately, advanced cloud-based data processing, sensor design and on-board computing now enable highly-sensitive multi-modal systems with real-time data acquisition and integration. Microsoft Premonition offers a surveillance platform that autonomously lures, identifies and selectively captures arthropods for downstream studies, including metagenomics. With Gates- Foundation support, we are deploying this system in Tanzania to enhance malaria vector surveillance. Here, the proposal is extending the premonition platform to investigate local associations between climate, land-use and mosquito-borne diseases.
Climate Change
Combination of Interventions
Modeling
Surveillance
Feb 2023 — Feb 2026
$1.92M