As over 70% of emerging infectious diseases are caused by parasites or pathogens transmitted from animals to humans (leading to 'zoonotic'infections), a fundamental issues for public health is identifying the drivers leading to zoonotic diseases in humans. Cross-species transmission of infectious agents depends on numerous traits of hosts, their infectious agents, and environmental factors defining the external context of disease. Previous studies identifying predictors of cross-species transmission have been limited by a focus on single infectious diseases (e.g., rabies, Lyme disease) at restricted spatial scales, in part because large-scale analyses spanning numerous host species and infectious agents are precluded by the many complex interactions, autocorrelations, and sampling biases common in multivariate, high-dimensional data. The proposed research confronts these computational limitations through the innovative application of machine learning algorithms. Specifically, analyses will address three outstanding and interrelated questions in global health: (1) What characteristics signal a predisposition of mammalian host species to be reservoirs of zoonotic disease?;(2) What traits among infectious agents predict their potential to cause zoonotic infection?;(3) What are the most important environmental and anthropogenic predictors of zoonotic outbreaks globally? Analyses will apply a series of supervised, unsupervised and semi-supervised machine learning algorithms to new, global-scale databases containing biological, ecological, environmental, and anthropogenic data for three groups of mammalian hosts (primates, carnivores, and ungulates) and their zoonotic infectious agents. A long-term goal of this research is to empirically develop "rules of thumb" about zoonotic diseases by highlighting the key traits of mammalian hosts, infectious agents, and the environmental and human factors describing zoonotic outbreaks in recent history. Ultimately, research proposed herein will provide a basis for predicting the geographic locations, infectious agents, and animal reservoirs from which future zoonoses will emerge.

Public Health Relevance

This project proposes to investigate the factors driving zoonotic disease outbreaks and cross-species transmission from wild mammals into humans through the innovative application of machine learning algorithms to newly published data describing hundreds of infectious agents, their mammalian host species, human populations, and the global environment. Ultimately, this project aims to predict the locations and species from which future diseases will emerge, and is therefore directly relevant for the improvement of human health.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
5F32GM087811-02
Application #
8314607
Study Section
Special Emphasis Panel (ZRG1-F14-C (20))
Program Officer
Flicker, Paula F
Project Start
2011-07-11
Project End
2014-07-10
Budget Start
2012-07-11
Budget End
2013-07-10
Support Year
2
Fiscal Year
2012
Total Cost
$53,942
Indirect Cost
Name
University of Georgia
Department
None
Type
Other Domestic Higher Education
DUNS #
004315578
City
Athens
State
GA
Country
United States
Zip Code
30602