The value of decreasing the duration of the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection

PLoS Comput Biol. 2021 Jan 7;17(1):e1008470. doi: 10.1371/journal.pcbi.1008470. eCollection 2021 Jan.

Abstract

Finding medications or vaccines that may decrease the infectious period of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could potentially reduce transmission in the broader population. We developed a computational model of the U.S. simulating the spread of SARS-CoV-2 and the potential clinical and economic impact of reducing the infectious period duration. Simulation experiments found that reducing the average infectious period duration could avert a median of 442,852 [treating 25% of symptomatic cases, reducing by 0.5 days, reproductive number (R0) 3.5, and starting treatment when 15% of the population has been exposed] to 44.4 million SARS-CoV-2 cases (treating 75% of all infected cases, reducing by 3.5 days, R0 2.0). With R0 2.5, reducing the average infectious period duration by 0.5 days for 25% of symptomatic cases averted 1.4 million cases and 99,398 hospitalizations; increasing to 75% of symptomatic cases averted 2.8 million cases. At $500/person, treating 25% of symptomatic cases saved $209.5 billion (societal perspective). Further reducing the average infectious period duration by 3.5 days averted 7.4 million cases (treating 25% of symptomatic cases). Expanding treatment to 75% of all infected cases, including asymptomatic infections (R0 2.5), averted 35.9 million cases and 4 million hospitalizations, saving $48.8 billion (societal perspective and starting treatment after 5% of the population has been exposed). Our study quantifies the potential effects of reducing the SARS-CoV-2 infectious period duration.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / epidemiology
  • COVID-19 / transmission*
  • COVID-19 Drug Treatment*
  • COVID-19 Vaccines / therapeutic use
  • Computational Biology
  • Computer Simulation
  • Humans
  • Models, Biological*
  • Pandemics* / prevention & control
  • Pandemics* / statistics & numerical data
  • SARS-CoV-2* / drug effects
  • Time Factors
  • United States / epidemiology
  • Virus Shedding / drug effects

Substances

  • COVID-19 Vaccines