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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Published on: September 25, 2021

Network models: an underutilized tool in wildlife epidemiology?

Meggan E Craft1, Damien Caillaud

  • 1Boyd Orr Centre for Population and Ecosystem Heath, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK.

Interdisciplinary Perspectives on Infectious Diseases
|April 30, 2011
PubMed
Summary

Contact network epidemiology, while popular for human diseases, is underutilized in wildlife. This paper reviews advances and advocates for increased use of network approaches in wildlife epidemiology through collaboration.

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Area of Science:

  • Ecology
  • Epidemiology
  • Network Science

Background:

  • Contact network epidemiology is widely used for human infectious diseases.
  • Its application in wildlife epidemiology remains limited.
  • Understanding disease transmission in wildlife is crucial for conservation and public health.

Purpose of the Study:

  • To explore differences in data collection for human vs. wildlife populations.
  • To update on recent network-based advances in wildlife epidemiology.
  • To discuss reasons for underutilization and advocate for increased network use in wildlife.

Main Methods:

  • Review of existing literature on contact network epidemiology.
  • Analysis of data collection challenges in wildlife studies.
  • Discussion of network modeling applications in wildlife disease ecology.

Main Results:

  • Significant differences exist in data acquisition between human and wildlife studies.
  • Recent advances show the potential of network approaches in wildlife epidemiology.
  • Underutilization stems from data limitations and interdisciplinary gaps.

Conclusions:

  • Network approaches offer valuable insights into wildlife disease dynamics.
  • Increased cross-disciplinary collaboration between field biologists and modelers is essential.
  • Future research should focus on overcoming data challenges and integrating network models.