Models of epidemics: when contact repetition and clustering should be included

Timo Smieszek1, Lena Fiebig, Roland W Scholz

  • 1Institute for Environmental Decisions, Natural and Social Science Interface, ETH Zurich, Universitaetsstrasse 22, 8092 Zurich, Switzerland. timo.smieszek@env.ethz.ch

Abstract

Insights

Disease spread models differ based on contact patterns. Repetitive or clustered contacts significantly impact outbreak size, especially with low transmission or contact rates, necessitating careful model selection for diseases like MRSA or Ebola.

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Infectious Disease Dynamics

Background:

  • Infectious disease spread is influenced by biological and social factors, including contact patterns.
  • Repetitive and clustered contacts are known to affect transmission but their role in realistic disease modeling is not fully understood.

Purpose of the Study:

  • To compare individual-based models with random mixing versus those incorporating repetitive and clustered contacts.
  • To determine how contact patterns influence outbreak size under varying transmission probabilities and contact rates.

Main Methods:

  • Comparison of two individual-based models: random mixing vs. repetitive contacts (with and without clustering).
  • Systematic testing of parameters: transmission probability, contacts per day, infectious period duration, clustering levels, and repetitive contact proportions.

Main Results:

  • Differences between models are most pronounced with low daily contacts and low transmission probabilities.
  • Contact number and transmission probability significantly influence model divergence more than infectious period length.
  • Even minor contact repetition/clustering can cause substantial deviations from random mixing models.

Conclusions:

  • Random mixing models suffice for high contact/transmission rates (e.g., measles) or very short infectious periods (e.g., Norovirus).
  • Models with repetitive contacts are crucial when daily contact numbers or transmission probabilities are low (e.g., MRSA, Ebola).
  • Accurate disease modeling requires considering the actual contact structure for specific pathogens and transmission scenarios.

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