Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing

PLoS Comput Biol. 2021 Oct 28;17(10):e1009518. doi: 10.1371/journal.pcbi.1009518. eCollection 2021 Oct.

Abstract

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies.

Publication types

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

MeSH terms

  • COVID-19 / epidemiology
  • COVID-19 / prevention & control*
  • COVID-19 Testing / methods
  • Communicable Disease Control / methods
  • Computational Biology
  • Computer Simulation
  • Cost-Benefit Analysis
  • Humans
  • Models, Biological
  • Pandemics / prevention & control*
  • Physical Distancing
  • SARS-CoV-2*

Grants and funding

The authors acknowledge support from NSF COVID-19 RAPID awards 2028301 (KS) and 2037885 (KS), the NSF-NIH-NIFA-BBSRC Ecology and Evolution of Infectious Diseases (EEID) program award DEB 1911962 BB/T004312/1 (MJF, KS, MJT, CPJ), the Huck Institutes for the Life Sciences at The Pennsylvania State University (KS), the Li Ka Shing Foundation (WJMP), the National Institutes of Health (CV) and the U.S. Geological Survey (MCR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.