A model for the co-evolution of dynamic social networks and infectious disease dynamics

Hendrik Nunner*, Vincent Buskens, Mirjam Kretzschmar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Recent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that networking behavior in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations of a specific model case show that: (i) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes; (ii) small changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not; (iii) high benefits for social connections create more ties per agent, providing large numbers of potential transmission routes and opportunities for the disease to travel faster, and (iv) higher costs of maintaining ties with infected others reduce final size of epidemics only when benefits of indirect ties are relatively low. These findings suggest a complex interplay between social network, health behavior, and infectious disease dynamics. Furthermore, they contribute to solving the issue that neglect of explicit health behavior in models of disease spread may create mismatches between observed transmissibility and epidemic sizes of model predictions.

Original languageEnglish
Article number19
Pages (from-to)1-33
JournalComputational Social Networks
Volume8
Issue number1
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Network formation
  • Complex networks
  • Network dynamics
  • Epidemics
  • Infectious diseases
  • Health behavior
  • Risk perception
  • Agent-based simulation

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