Optimizing testing for COVID-19 in India

PLoS Comput Biol. 2021 Jul 22;17(7):e1009126. doi: 10.1371/journal.pcbi.1009126. eCollection 2021 Jul.

Abstract

COVID-19 testing across India uses a mix of two types of tests. Rapid Antigen Tests (RATs) are relatively inexpensive point-of-care lateral-flow-assay tests, but they are also less sensitive. The reverse-transcriptase polymerase-chain-reaction (RT-PCR) test has close to 100% sensitivity and specificity in a laboratory setting, but delays in returning results, as well as increased costs relative to RATs, may vitiate this advantage. India-wide, about 49% of COVID-19 tests are RATs, but some Indian states, including the large states of Uttar Pradesh (pop. 227.9 million) and Bihar (pop. 121.3 million) use a much higher proportion of such tests. Here we show, using simulations based on epidemiological network models, that the judicious use of RATs can yield epidemiological outcomes comparable to those obtained through RT-PCR-based testing and isolation of positives, provided a few conditions are met. These are (a) that RAT test sensitivity is not too low, (b) that a reasonably large fraction of the population, of order 0.5% per day, can be tested, (c) that those testing positive are isolated for a sufficient duration, and that (d) testing is accompanied by other non-pharmaceutical interventions for increased effectiveness. We assess optimal testing regimes, taking into account test sensitivity and specificity, background seroprevalence and current test pricing. We find, surprisingly, that even 100% RAT test regimes should be acceptable, from both an epidemiological as well as a economic standpoint, provided the conditions outlined above are met.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antigens, Viral / analysis
  • COVID-19 Testing* / methods
  • COVID-19 Testing* / standards
  • COVID-19 Testing* / statistics & numerical data
  • COVID-19* / diagnosis
  • COVID-19* / epidemiology
  • Computational Biology
  • Humans
  • India
  • Models, Statistical*
  • Point-of-Care Testing
  • Reverse Transcriptase Polymerase Chain Reaction
  • SARS-CoV-2
  • Sensitivity and Specificity

Substances

  • Antigens, Viral

Grants and funding

GIM acknowledges support of the Bill and Melinda Gates Foundation, Grant No: R/BMG/PHY/GMN/20, while PC acknowledges support from Ashoka University. SK acknowledges support of the Department of Atomic Energy, Government of India, under Project Identification No. RTI 4006, and of the Simons Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.