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Machine Learning to Predict Zoonotic Disease

Lead Scientist(s): Dr. Barbara A. Han

Why do the majority of human infectious diseases originate from wildlife? What distinguishes the small fraction of species that carry and transmit zoonoses to humans? Intrinsic organismal characteristics (e.g., life history, ecological, physiological traits) recapitulate a long evolutionary history that may signal species' capacity to be future reservoirs, vectors, or microbial agents of zoonoses (human diseases with animal origins). By examining particular species groups (mammal orders), vector groups (ticks, mosquitoes), and pathogen and parasite types, this work aims to understand the biological underpinnings of zoonotic diseases.

Our work is responsive to current global health events, such as the recent outbreaks of Ebola virus and Zika virus, to extract actionable, data-driven predictions to inform management and public health preparedness. We combine tools from data science, machine learning, and dynamical modeling to generate infectious disease intelligence and a develop a predictive strategy to confront growing threats to global health.