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Area of Science:

  • Ecology and Evolutionary Biology
  • Epidemiology
  • Computational Biology

Background:

  • Environmentally mediated transmission is crucial for disease spread, especially for persistent pathogens.
  • Existing mobility models often assume homogeneous exposure, neglecting spatial and behavioral influences on transmission.
  • Distinguishing transmission pathways from overlapping host movements remains challenging.

Purpose of the Study:

  • To extend indirect contact models by incorporating host movement behavior variations.
  • To examine the effects of movement behavior on indirect transmission probability and disease dynamics.
  • To compare four models with differing assumptions on movement behavior's influence on transmission.

Main Methods:

  • Developed four models: pathogen decay, high-use area, behavioral, and integrated (spatial & behavioral constraints).
  • Applied models to GPS telemetry data from wild pigs in Florida.
  • Simulated disease dynamics using SEIR models for short (Influenza A) and long-lived (Brucella suis) pathogens.

Main Results:

  • Behavioral and integrated models significantly reduced contact numbers (over 70%) compared to the pathogen decay model.
  • Network metrics like edge density, transitivity, and assortativity were altered by movement behavior assumptions.
  • Effective reproduction number (R0) decreased substantially, with peak incidence delayed by ~21 days, consistent across pathogen persistence scenarios.

Conclusions:

  • Assumptions linking host movement data to pathogen transmission critically impact network structure and epidemic forecasts.
  • Mechanistically defining indirect transmission pathways is essential for accurate disease modeling.
  • Movement behavior significantly modulates the potential for environmentally mediated disease spread.