Estimation of the case fatality rate based on stratification for the COVID-19 outbreak

PLoS One. 2021 Feb 22;16(2):e0246921. doi: 10.1371/journal.pone.0246921. eCollection 2021.

Abstract

This work is motivated by the recent worldwide pandemic of the novel coronavirus disease (COVID-19). When an epidemiological disease is prevalent, estimating the case fatality rate, the proportion of deaths out of the total cases, accurately and quickly is important as the case fatality rate is one of the crucial indicators of the risk of a disease. In this work, we propose an alternative estimator of the case fatality rate that provides more accurate estimate during an outbreak by reducing the downward bias (underestimation) of the naive CFR, the proportion of deaths out of confirmed cases at each time point, which is the most commonly used estimator due to the simplicity. The proposed estimator is designed to achieve the availability of real-time update by using the commonly reported quantities, the numbers of confirmed, cured, deceased cases, in the computation. To enhance the accuracy, the proposed estimator adapts a stratification, which allows the estimator to use information from heterogeneous strata separately. By the COVID-19 cases of China, South Korea and the United States, we numerically show the proposed stratification-based estimator plays a role of providing an early warning about the severity of a epidemiological disease that estimates the final case fatality rate accurately and shows faster convergence to the final case fatality rate.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / mortality*
  • China / epidemiology
  • Humans
  • Models, Theoretical*
  • Pandemics / statistics & numerical data*
  • Republic of Korea / epidemiology
  • United States / epidemiology

Grants and funding

W.J. and B.K. were supported by the National Research Foundation of Korea (NRF) grant and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Korea government(MSIT) and the Ministry of Health & Welfare, Republic of Korea (No. 2017R1A2B2012816, HI19C0378). S.J. and B.K. were supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C2002256).