The global health community recognizes the issue of zoonotic spillover as the leading cause of pandemic occurrence, but it is still a poorly understand phenomena. Zoonotic spillover, or the transmission of a pathogen from a vertebrate animal to a human, presents a global public health burden and is a poorly understood phenomenon. A spillover event requires several factors to align, including the ecological, epidemiological, and behavioral determinants of pathogen exposure, and human factors that affect susceptibility to infection. A spillover event is the most probable cause of the initial case of COVID-19. These happenings occur around the world constantly, and have the potential to lead to the next worldwide pandemic. Factors like climate change and economic development are also rapidly increasing human and animal interaction. New surveillance technologies and risk analytics must be developed to support public health efforts and keep up with an increasingly interconnected world between humans and animals.
HSR.health is developing a new solution that leverages artificial intelligence and machine learning to respond faster and more accurately to zoonotic spillover events in the 21st century. This solution utilizes a diverse range of data sources to create a comprehensive model for the prediction of spillover risk. The model’s analytics can then be used to identify geographical areas at risk. By combining these analytics with patient level data on disease incidence and symptomatology, public health decision makers can respond with surgical precision when and where a zoonotic spillover event may occur.