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Complex system modelling for veterinary epidemiology.

Cristina Lanzas1, Shi Chen2

  • 1Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, 2407 River Drive, Knoxville, TN 37996, USA; National Institute for Mathematical and Biological Synthesis, University of Tennessee, 1122 Volunteer Blvd, Knoxville, TN 37996, USA.

Preventive Veterinary Medicine
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Summary
This summary is machine-generated.

Mathematical modeling in infectious disease epidemiology helps understand complex transmission dynamics. Advanced methods like agent-based modeling offer new insights for effective public health strategies.

Keywords:
Agent-based modelComplex systemsMathematical modellingNetworkVeterinary epidemiology

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Area of Science:

  • Epidemiology
  • Systems Science
  • Computational Biology

Background:

  • Mathematical models have a long history in infectious disease epidemiology.
  • Understanding pathogen transmission complexity is crucial for effective mitigation.
  • Advancements in computing and data generation have expanded modeling capabilities.

Purpose of the Study:

  • To review recent modeling approaches in systems science for infectious disease epidemiology.
  • To discuss the principles, advantages, and challenges of agent-based, network, and compartmental modeling.
  • To highlight the utility of these models in addressing complex spatio-temporal dynamics and integrating diverse datasets.

Main Methods:

  • Discussion of agent-based modeling (ABM) as a simulation technique.
  • Explanation of ABM's focus on individual behaviors and interactions.
  • Comparison with network and compartmental modeling approaches within systems science.

Main Results:

  • Agent-based models are effective for simulating hierarchical systems.
  • These models can integrate multiple scales and datasets, including proximity logger and GPS data.
  • Recent modeling approaches enhance understanding of disease transmission dynamics.

Conclusions:

  • Mathematical modeling, particularly agent-based modeling, provides powerful tools for infectious disease epidemiology.
  • These advanced techniques offer significant insights into disease determinants and mitigation strategies.
  • The integration of novel data sources further strengthens the application of these models in public health.