ABSTRACT
Aim: The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) in estimating the severity and epidemic trend of COVID-19 in China. Subjects and methods: Epidemiological data on COVID-19 in China and Hubei Province were obtained from the National Health Commission of the People's Republic of China from January 20, 2020, to March 31, 2020. The number of daily new confirmed cases, daily confirmed deaths, daily recovered cases, the proportion of daily deaths and total deaths of discharged cases were collected, and the total discharge case fatality rate (tDCFR), daily discharge case fatality rate (dDCFR), and stage-discharge case fatality rate (sDCFR) were calculated. We used the R software (version 3.6.3, R core team) to apply a trimmed exact linear time method to search for changes in the mean and variance of dDCFR in order to estimate the pandemic phase from dDCFR. Results: The tDCFR of COVID-19 in China was 4.16% until March 31, 2020. According to the pattern of dDCFR, the pandemic was divided into four phases: the transmission phase (from January 20 to February 2), the epidemic phase (from February 3 to February 14), the decline phase (from February 15 to February 22), and the sporadic phase (from February 23 to March 31). The sDCFR for these four phases was 43.18% (CI 39.82-46.54%), 13.23% (CI 12.52-13.94%), 5.86% (CI 5.49-6.22%), and 1.61% (CI 1.50-1.72%), respectively. Conclusion: DCFR has great value in assessing the severity and epidemic trend of COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01895-4.
ABSTRACT
Background: The coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented demands on local public health units in Ontario, Canada, one of which was the need for in-house epidemiological modelling capabilities. The objective of this study is to develop a native Windows desktop app for epidemiological modelling, to be used by public health unit epidemiologists to predict COVID-19 transmission in Durham Region. Methods: The developed app is an implementation of a multi-stratified compartmental epidemiological model that can accommodate multiple virus variants and levels of vaccination, as well as public health measures such as physical distancing, contact tracing followed by quarantine and testing followed by isolation. It was used to investigate the effects of different factors on COVID-19 transmission, including vaccination coverage, vaccine effectiveness, waning of vaccine-induced immunity and the advent of the Omicron variant. The simulation start date was November 22, 2021. Results: For the Delta variant, at least 90% of the population would need to be vaccinated to achieve herd immunity. A Delta-variant-only epidemiological curve would be flattened from the start in the absence of immunity waning and within six months in the presence of immunity waning. The percentage of infections caused by the Omicron variant was forecast to increase from 1% to 97% in the first month of the simulation. Total Omicron infections were forecasted to be reduced, respectively, by 26% or 41% if 3,000 or 5,000 booster doses were administered per day. Conclusion: For the Delta variant, both natural and vaccination-induced immunity are necessary to achieve herd immunity, and waning of vaccine-induced immunity lengthens the time necessary to reach herd immunity. In the absence of additional public health measures, a wave driven by the Omicron variant was predicted to pose significant public health challenges with infections predicted to peak in 2-3 months from the start of the simulation, depending on the rate of administration of booster doses.