Machine Learning to Predict Zoonotic Disease
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? Our lab focuses on identifying intrinsic organismal characteristics (e.g., life history, ecological, physiological traits) that signal their potential 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 uncovers the biological underpinnings of intrinsic zoonotic potential.
Our work is also 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.
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Macroecology of Zoonotic Disease
Zoonotic diseases are distributed across space, time, and species. Our lab focuses on answering outstanding 'who, what, where' questions about zoonotic diseases. Where are zoonotic reservoirs currently distributed, and where are their hotspots and coldspots? What particular species pose the greatest spillover risk to humans? When do zoonotic disease threats grow and when do they diminish? We pursue these questions with two goals: 1) to inform global health preparedness and improve ecologically comprehensive disease management, and 2) to draw out mechanistic hypotheses about the outbreak and emergence process from empirical data.
Some active projects include one led by Dr. Sarah Bowden examining the patterns and dynamics of land-use change on the incidence of mosquito-borne zoonotic viruses; ongoing analyses to understand the emergence ecology of Ebola virus; identifying wild reservoirs to prevent long-term endemicity of Zika virus in the Americas; predicting novel tick vectors and understanding why some ticks are better than others at transmitting zoonoses; and investigating the assembly and interaction of viromes found within a widespread mouse reservoir and a widespread tick vector to explore how viromes influence pathogen transmission.
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Disease Ecology of Wildlife
While the majority of our work centers on zoonotic diseases, ongoing collaborations explore the ecology of diseases found only in wildlife. Previous work examined amphibians are important indicators of ecosystem health that are declining globally due to pathogenic threats such as Batrachochytrium dendrobatidis (Bd), a fungal pathogen. The disease, chytridiomycosis, is estimated to have impacted hundreds of frog species worldwide and is widely considered among the greatest threats to global biodiversity. In previous work, Dr. Han investigated the impact of Bd infection on host behaviors, such as aggregation, thermoregulation, and anti-predator behaviors, and the consequences of these changes for host community stability and diversity.
An active project examines the role of amphibians as voracious mosquito predators, focusing on the negative feedbacks caused by mosquito-repellents on the ability of amphibians to provide vector control as a crucial ecosystem service.
Another active research area applies ecoinformatics and machine learning approaches to predict bat species, in North America and globally, that are susceptible to developing White Nose Syndrome and vulnerable to declines caused by Pseudogymnoascus descructans infection.
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