Weekends as social distancing and their effect on the spread of influenza

  • Philip C. Cooley
  • Sarah M. Bartsch
  • Shawn T. Brown
  • William D. Wheaton
  • Diane K. Wagener
  • Bruce Y. Lee
Manuscript
  • 210 Downloads

Abstract

Many published influenza models treat each simulation day as a weekday and do not distinguish weekend days. Consequently, the weekend effect on influenza transmission has not been fully explored. To assess whether distinguishing between weekday and weekend transmissions in simulation models of flu-like infectious disease models matters, this study uses an agent-based model of the Chicago, Illinois metropolitan area. Our study assesses whether including weekend effects is offset by increases in weekend contact patterns and if implementing 3-day weekends dampens disease transmission enough to warrant its use as a containment strategy. Results indicate that ignoring weekend behaviors without incorporating increases in community-based non-school contacts (i.e., compensatory behaviors) causes the peak case incidence day to occur 7 days earlier and can reduce the peak attack rate by as much as 60 %. These results are sensitive to the proportion of symptomatic cases that are assumed to remain at home until they recover. The 3-day weekend intervention has interesting possibilities, but the benefits may only be effective for mild epidemics. However, a 3-day weekend for schools would be less detrimental to the educational process than sustained permanent closing because student and teacher contact is maintained throughout the epidemic period. Also, a 4-day school and work week may be more easily accommodated by many types of schools and businesses. On the other hand, an additional day per week of school closure could result in substantial societal costs, with lost productivity and child care costs outweighing the savings of preventing influenza cases.

Keywords

Influenza Influenza transmission Epidemics Weekends Intervention strategy Agent-based model 

Notes

Acknowledgments

This work was supported by the Agency for Healthcare Research and Quality (AHRQ) via grant R01HS023317 and the National Institute of Child Health and Human Development (NICHD) and the Global Obesity Prevention Center (GOPC) via grant U54HD070725. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. The authors thank Craig R. Hollingsworth for technical writing and editing assistance.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Philip C. Cooley
    • 1
  • Sarah M. Bartsch
    • 2
  • Shawn T. Brown
    • 3
  • William D. Wheaton
    • 4
  • Diane K. Wagener
    • 5
  • Bruce Y. Lee
    • 2
  1. 1.Research Computing DivisionRTI InternationalResearch Triangle ParkUSA
  2. 2.Public Health Computational and Operations ResearchJohns Hopkins UniversityBaltimoreUSA
  3. 3.Pittsburgh Supercomputing CenterCarnegie Mellon UniversityPittsburghUSA
  4. 4.Geospatial Science and Technology ProgramRTI InternationalResearch Triangle ParkUSA
  5. 5.Genomics, Statistical Genetics and Environmental Research Program RTI International (Retired)San DiegoUSA

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