Modeling strict age-targeted mitigation strategies for COVID-19

PLoS One. 2020 Jul 24;15(7):e0236237. doi: 10.1371/journal.pone.0236237. eCollection 2020.


We use a simple SIR-like epidemic model integrating known age-contact patterns for the United States to model the effect of age-targeted mitigation strategies for a COVID-19-like epidemic. We find that, among strategies which end with population immunity, strict age-targeted mitigation strategies have the potential to greatly reduce mortalities and ICU utilization for natural parameter choices.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Age Factors*
  • Betacoronavirus
  • COVID-19
  • Coronavirus Infections / mortality
  • Coronavirus Infections / prevention & control*
  • Humans
  • Intensive Care Units / statistics & numerical data
  • Models, Theoretical*
  • Pandemics / prevention & control*
  • Pneumonia, Viral / mortality
  • Pneumonia, Viral / prevention & control*
  • SARS-CoV-2
  • United States

Grants and funding

Maria Chikina received no specific funding for this work. Wesley Pegden is funded by the National Science Foundation (DMS-1700365), which in the Mathematical Sciences is expected to be acknowledged in all research projects done during the funding period, through a statement of the form \supported by NSF DMS-1700365", or something similar. Funding statement: The National Science Foundation played no role in study design, data col- lection and analysis, decision to publish, or preparation of the manuscript.