Estimating under-reporting of COVID-19 cases in Indian states: an approach using a delay-adjusted case fatality ratio

BMJ Open. 2021 Jan 20;11(1):e042584. doi: 10.1136/bmjopen-2020-042584.

Abstract

Objectives: The COVID-19 pandemic has spread to all states in India. Due to limitations in testing coverage, the true extent of the spread may not be fully reflected in the reported cases. In this study, we obtain time-varying estimates of the fraction of COVID-19 infections reported in the different states.

Methods: Following a methodology developed in prior work, we use a delay-adjusted case fatality ratio to estimate the true fraction of cases reported in different states. We also develop a delay adjusted test positivity estimation method and study the relationship between the estimated test positivity rate for each state and the estimated fraction of cases reported.

Setting: We apply this method of analysis to all Indian states reporting at least 100 deaths as of 10 October 2020.

Results: Our analysis suggests that delay-adjusted case fatality ratios observed in different states range from 0.47% to 3.55%. The estimated fraction of cases reported in different states ranges from 39% to 100% for an assumed baseline case fatality ratio of 1.38%, from 18.6% to 100% for an assumed baseline case fatality ratio of 0.66%, and from 2.8% to 19.7% for an assumed baseline case fatality ratio of 0.1%. We also demonstrate a statistically significant negative relationship between the fraction of cases reported in each state and the testing positivity rate.

Conclusions: The estimates provide a means to quantify and compare the trends of reporting and the true level of current infections in different states. This information may be used to guide policies for prioritising testing in different states, and also to analyse the time-varying effects of different quarantine measures adopted in different states.

Keywords: infectious diseases; public health; statistics & research methods.

MeSH terms

  • Bias
  • COVID-19 / epidemiology*
  • COVID-19 / mortality*
  • COVID-19 / virology
  • Humans
  • India / epidemiology
  • Mortality*
  • SARS-CoV-2 / genetics