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Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated

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  • 1Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, United States of America.

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This study introduces a novel pathogen-centric patch dynamics model, offering a more holistic view of infectious disease spread. This framework enhances traditional models by integrating within-host and between-host dynamics for better simulation and intervention evaluation.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Biology

Background:

  • Traditional Susceptible-Infected-Recovered (SIR)-type models focus on host populations.
  • These models track changes within compartments but can oversimplify transmission dynamics.

Purpose of the Study:

  • To propose an alternative modeling framework for infectious disease dynamics from the pathogen's perspective.
  • To provide a more comprehensive and flexible approach than existing compartment models.

Main Methods:

  • Developed a patch dynamics modeling framework where each patch represents pathogen population dynamics.
  • Incorporated four key mechanisms: replication, death, inflow, and outflow within each patch.
  • Demonstrated the framework's ability to integrate with agent-based models and utilize diverse data sources for parameterization.

Main Results:

  • The proposed patch dynamics model generalizes SIR-type models, viewing them as a discretized, cross-sectional case.
  • The framework naturally distinguishes between within-host and between-host pathogen transmission processes.
  • Successfully applied the model in proof-of-concept studies for sexually transmitted and healthcare-acquired infections.

Conclusions:

  • The patch dynamics framework offers a more holistic viewpoint for understanding disease dynamics across biological scales.
  • This approach provides theoretical explanations and generates novel insights, improving simulation of complex scenarios like pandemics.
  • The modular design and parameterization flexibility allow for adaptation to various infectious diseases and data types.