Overview
Conceptualized as a forward-thinking solution to anticipate and mitigate the risks associated with vector-borne diseases (VBDs), the AI-based Early Warning and Surveillance System project embodies DTDA's commitment to leveraging artificial intelligence in the public health domain. It incorporates a comprehensive One Health approach that considers the interlinkage between human, animal, and environmental health, central to predicting and responding to VBD outbreaks such as malaria, dengue, and cholera.Project Description
With a projected duration of two and a half years, this concept project employs a meticulous methodology for VBD prevention. The system will gather and analyze historical VBD data to train an AI model, develop standardized protocols for outbreak prediction indicators, and disseminate vital information to key stakeholders.
Data sources, including environmental (satellite and meteorological data), entomological (population density of vectors, vector behavior), social media mining, demographic factors, health facilities data, and altitude variables, will inform the AI model. The model will undergo regular reviews and improvements to ensure precision in predicting VBD outbreaks.
The project adheres to the internationally set 7-1-7 target for outbreak response—detecting suspected outbreaks within 7 days, initiating an investigation within 1 day, and launching an effective response within 7 days.
Donors and Partners
This project is currently in the concept note stage, and funding partners and collaborations are yet to be established.Project Outcomes and Impact
The AI-based Early Warning and Surveillance System aims to transform public health responses to VBD outbreaks, with outputs ranging from disease outbreak monitoring to automated case detection and early reporting. It will identify hotspots of vulnerable subgroups, enabling targeted interventions such as vector control and health access and education initiatives.
