Pilot machine learning solutions in the process of being developed to assess and predict locations where known drivers of zoonotic disease emergence, spillover and outbreaks are likely to occur. This solution will utilise open source environmental, epidemiology and demographic data including deforestation data, disease outbreaks reports, wildife market location evaluations and regional infrastructure developments. The ML algorithms will be tailored to extract features within defined geographical coordinates, utilizing spatial analytics to assess proximity to relevant geographical entities. The algorithm will then seek to identify the importance of geographic, demographic, environmental or other factors in predicting disease hotspots.

The tool helps to assess wildlife markets and trade situations in the Asia-Pacific region to risks of future zoonotic outbreaks. The framework is intended to provide guidance to the region’s governments to assess the relative risk of potential new incidents of serious emerging infectious diseases associated with the trade in wildlife. The risk matrix, published in the One Health scientific journal, will initially be used to analyze wildlife markets in the Asia Pacific region. The evaluation tool recognizes the connection between people, nature and their shared environment, and provides a way for policy makers to take a holistic approach to ecosystem, animal and human health.