The illusion of personal health decisions for infectious disease management: disease spread in social contact networks

Matthew Michalska-Smith, Eva A. Enns, Lauren A. White, Marie L.J. Gilbertson, Meggan E. Craft

Research output: Contribution to journalArticlepeer-review


Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.

Original languageEnglish (US)
Article number221122
JournalRoyal Society Open Science
Issue number3
StatePublished - 2023

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation (DEB 1654609 and 2030509), a University of Minnesota Office of Academic Clinical Affairs COVID-19 Rapid Response grant, the Office of the Director, National Institutes of Health (NIH T32OD010993), the University of Minnesota Informatics Institute MnDRIVE programme, the Van Sloun Foundation, and by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation (DBI 1639145). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Acknowledgements

Publisher Copyright:
© 2023 The Authors.


  • disease dynamics
  • disease mitigation
  • interaction types
  • multi-layer contact network
  • network structure
  • risk tolerance

PubMed: MeSH publication types

  • Journal Article


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