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Capturing Household Transmission in Compartmental Models of Infectious Disease

In: Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases

Author

Listed:
  • Jude Bayham

    (College of Agriculture, California State University, Chico)

  • Eli P. Fenichel

    (Yale University, Yale School of Forestry and Environmental Studies)

Abstract

Social distancing policies may mitigate transmission of infectious disease by shifting individuals time spent in public into household environments. However, the efficacy of such a policy depends on the transmission differential between public and household environments. We extend the standard compartmental model of infectious disease with heterogeneous mixing to explicitly account for the health state of households. Our model highlights the fact that only households with an infectious individual pose a transmission risk to other household members. Moreover, susceptible households become infectious at a rate that depends on household size and the health status of the household members. We demonstrate our model by simulating an epidemic similar to the A/H1N1 2009 outbreak using empirical mixing patterns derived from time-use data in the United States. We find that household transmission accounts for 12–23 % of total cases. These results suggest that while social distancing policies encourage individuals to spend more time at home, the reduction of time in public improves public health outcomes on balance.

Suggested Citation

  • Jude Bayham & Eli P. Fenichel, 2016. "Capturing Household Transmission in Compartmental Models of Infectious Disease," Springer Books, in: Gerardo Chowell & James M. Hyman (ed.), Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases, pages 329-340, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-40413-4_20
    DOI: 10.1007/978-3-319-40413-4_20
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