Early estimation of the case fatality rate of COVID-19 in mainland China: a data-driven analysis

Shu Yang1,2, Peihua Cao3, Peipei Du4

  • 1College of Medical Information Engineering, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.

Insights

Early COVID-19 case fatality rates (CFR) were estimated using statistical methods. Wuhan showed the highest CFR at 5.25%, while mainland China (excluding Hubei) had the lowest at 0.15%.

Area of Science:

  • Epidemiology
  • Infectious Diseases
  • Public Health

Background:

  • The novel coronavirus (SARS-CoV-2) caused a global pandemic starting in Wuhan, China.
  • Estimates for the case fatality rate (CFR) of COVID-19 were lacking in the early stages.
  • Previous coronaviruses like SARS-CoV and MERS-CoV had significant CFRs.

Purpose of the Study:

  • To estimate the case fatality rate (CFR) of COVID-19 during the early phase of the outbreak.
  • To compare COVID-19 CFR with those of SARS-CoV and MERS-CoV.

Main Methods:

  • A data-driven statistical approach was employed.
  • Daily confirmed COVID-19 cases and deaths were collected from January 10 to February 3, 2020.
  • Data was stratified into Wuhan, other Hubei cities, and other mainland China provinces, with linear regression applied to estimate CFR.

Main Results:

  • Estimated CFRs varied significantly by region.
  • Wuhan: 5.25% (95% CI: 4.98-5.51%).
  • Hubei (excluding Wuhan): 1.41% (95% CI: 1.38-1.45%).
  • Mainland China (excluding Hubei): 0.15% (95% CI: 0.12-0.18%).

Conclusions:

  • Early COVID-19 CFR estimates were lower than those of SARS-CoV and MERS-CoV.
  • Geographical variations in CFR were observed in the initial phase of the outbreak.
Abstract

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
429
Pie Chart01:04

Pie Chart

A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
15.7K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
499
Pareto Chart00:52

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
7.6K
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
243
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
823