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Spreading processes with mutations over multilayer networks.

Mansi Sood1, Anirudh Sridhar2, Rashad Eletreby3

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213.

Proceedings of the National Academy of Sciences of the United States of America
|June 8, 2023
PubMed
Summary
This summary is machine-generated.

Predicting infectious disease spread requires models that account for pathogen mutations and varied contact settings. Ignoring these factors, like pathogen evolution and diverse transmission risks, can lead to inaccurate predictions of epidemic dynamics.

Keywords:
agent-based modelsbranching processesmultilayer networksmutationsnetwork epidemics

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Predicting infectious disease dynamics during outbreaks is challenging, especially when countermeasures affect population interactions.
  • Existing epidemiological models often overlook pathogen mutation and heterogeneity in contact types, which are crucial for understanding disease spread.
  • Pathogen evolution and varying transmission risks in different settings (e.g., schools, workplaces) necessitate more complex modeling approaches.

Purpose of the Study:

  • To develop and analyze a multilayer, multistrain epidemiological model.
  • To simultaneously incorporate pathogen mutation pathways and setting-specific transmission risks.
  • To assess the impact of these factors on epidemic prediction and the effectiveness of mitigation strategies.

Main Methods:

  • Developed a multilayer, multistrain model integrating pathogen mutation and network layers representing different contact settings.
  • Derived key epidemiological parameters within this framework, assuming complete cross-immunity between strains.
  • Analyzed the model's predictions compared to simplified models that ignore strain or network heterogeneity.

Main Results:

  • Models that simplify heterogeneity in pathogen strains or contact networks can yield incorrect epidemic predictions.
  • The interplay between mitigation measures across different network layers (e.g., school closures, remote work) and the emergence of new pathogen strains is significant.
  • Evaluating the impact of public health interventions requires considering both transmission dynamics and evolutionary potential of the pathogen.

Conclusions:

  • Accurate prediction of infectious disease trajectories requires models that account for both pathogen evolution and the complex structure of social contacts.
  • Mitigation strategies must be assessed not only for their immediate impact on transmission but also for their influence on pathogen mutation and strain emergence.
  • Future epidemiological modeling should integrate multilayer networks and multistrain dynamics to better inform public health policy.