The project aims to assess and mitigate food safety risks in wholesale and wet markets in China, correlating these risks with zoonotic disease outbreaks using machine learning and data analysis.
Machine learning algorithms are employed to develop risk scores for individual markets based on historical food safety data, helping identify high-risk areas susceptible to zoonotic diseases.
The project aggregates and preprocesses over 4 million food safety test records from China's Administration for Market Regulation (AMR) using comprehensive clustering and regression analysis.
Risk scores are computed based on failure rates from AMR tests, specifically targeting markets with significant animal product volumes.
The project aims to provide actionable tools for policymakers, enhance food safety regulations, and improve public health measures against zoonotic diseases in Chinese markets.
By identifying high-risk markets and providing evidence-based insights, the project contributes to proactive measures against zoonotic disease transmission, crucial for public health management.
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