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Defining an epidemiological landscape by connecting host movement to pathogen transmission
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  • Kezia Manlove,
  • Mark Wilber,
  • Lauren White,
  • Guillaume Bastille-Rousseau,
  • Anni Yang,
  • Marie Gilbertson,
  • Meggan Craft,
  • Paul Cross,
  • George Wittemyer,
  • Kim Pepin
Kezia Manlove
Utah State University

Corresponding Author:[email protected]

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Mark Wilber
The University of Tennessee Knoxville Institute of Agriculture
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Lauren White
National Socio-Environmental Synthesis Center
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Guillaume Bastille-Rousseau
Southern Illinois University Carbondale
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Anni Yang
USDA-APHIS National Wildlife Research Center
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Marie Gilbertson
University of Minnesota Twin Cities
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Meggan Craft
University of Minnesota System
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Paul Cross
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George Wittemyer
Colorado State University
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Kim Pepin
United States Department of Agriculture Animal and Plant Health Inspection Service
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Environment drives the host movements that shape pathogen transmission through three mediating processes: host density, host mobility, and contact. These processes combine with pathogen life-history to give rise to an “epidemiological landscape” that determines spatial patterns of pathogen transmission. Yet despite its central role in transmission, strategies for predicting the epidemiological landscape from real-world data remain limited. Here, we develop the epidemiological landscape as an interface between movement ecology and spatial epidemiology. We propose a movement-pathogen pace-of-life heuristic for prioritizing the landscape’s central processes by positing that spatial dynamics for fast pace-of-life pathogens are best-approximated by the spatial ecology of host contacts; spatial dynamics for slower pace-of-life pathogens are best approximated by host densities; and spatial dynamics for pathogens with environmental reservoirs reflect a convolution of those densities with the spatial configuration of environmental reservoir sites. We then identify mechanisms that underpin the epidemiological landscape and match each mechanism to emerging tools from movement ecology. Finally, we outline workflows for describing the epidemiological landscape and using it to predict subsequent patterns of pathogen transmission. Our framework links transmission to environmental context, providing a scaffold for mechanistically understanding how environmental context can generate and shift existing patterns in spatial epidemiology.