Global patterns, predictors, and their dynamical consequences in zoonotic diseases of mammals

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Around the world, outbreaks of novel infectious diseases are increasing in frequency. The majority of infectious diseases emerging in humans originate from animal hosts. There are now thousands of scientific studies investigating animal-borne (zoonotic) disease threats, but to date there are few methods to predict in advance where new infectious disease threats are most likely to arise. In this project, researchers seek to understand why some animals harbor many more human pathogens compared to others; what kinds of pathogens are most likely to pose unforeseen disease threats to humans; and what kinds of environments may see large disease outbreak events in the future. To do this, researchers will analyze large databases on mammals, their pathogens, and environmental conditions to describe what features best predict animal-borne infectious diseases in humans. Researchers will also examine the theoretical consequences of these predictions using mathematical models, to understand how disease patterns are most likely to change over time. Instead of reacting to new diseases after they have emerged, the predictions and methodological advances developed in this project will immediately benefit society by informing us how future disease threats may be preempted.By combining machine learning and mathematical modeling with growing volumes of data on infectious diseases in wildlife and humans, this project will: (1) Identify intrinsic traits of hosts and pathogens and general environmental features that best predict patterns of zoonotic infection; (2) Identify particular mammal species and pathogenic agents likely to be undiscovered sources of future zoonoses; (3) Investigate the host, pathogen, and environmental covariates that distinguish infection from zoonotic disease in humans, and that combine to predict outbreak size; (4) Fit mathematical models (theory) to empirically observed patterns to derive potential underlying processes; (5) Investigate paradigmatic eco-epidemiological mechanisms through which empirically observed traits of hosts, pathogens, and changing environments influence transmission processes and disease dynamics.