An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation

About the Project

This project develops an AI-informed, community-grounded framework to understand and mitigate antimicrobial resistance (AMR) risks within a planetary health context. Building on the risk modelling from Project 1, we will conduct focus groups and interviews with communities in high- and low-risk zones to capture local knowledge, behaviours, and risk perceptions related to AMR. In parallel, key informant interviews with policymakers from the Ministry of Health, Department of Environment, and local…

councils will map current policies, surveillance gaps, and opportunities for intervention. Insights from these engagements will be integrated with outputs from advanced AI models (e.g., geospatial deep learning, graph neural networks, and explainable AI) to refine the AMR risk maps and ensure local relevance.

Supervisors:

Main Supervisor Professor Wong Kok Sheik, Monash University

Co-supervisors:

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