Related Experiment Video

Updated: Dec 13, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
03:53

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

1.7K

Geographical variation in case fatality rate and doubling time during the COVID-19 pandemic

A Mazumder1, M Arora1, M S Sra1

  • 1All India Institute of Medical Sciences (AIIMS), New Delhi, India.

Epidemiology and Infection
|July 28, 2020
PubMed

Insights

This study introduces a cumulative case fatality rate (cCFR) method for more stable epidemic analysis. It reveals significant international variations in COVID-19 outcomes and epidemic control.

Area of Science:

  • Epidemiology
  • Infectious Disease Dynamics
  • Public Health

Background:

  • Case fatality rate (CFR) and doubling time are critical epidemic metrics.
  • COVID-19 exhibits significant international variability in CFR and doubling time.
  • Traditional CFR calculations are inaccurate early in an epidemic due to unknown outcomes.

Purpose of the Study:

  • To introduce a cumulative case fatality rate (cCFR) for temporal stability.
  • To estimate daily average doubling time for COVID-19.
  • To analyze international variations in CFR and epidemic control.

Main Methods:

  • Calculated cumulative CFR using only patients with known outcomes.
  • Estimated daily average doubling time.
  • Compared cCFR and doubling times across different countries.

Main Results:

  • The cCFR method provides temporally stable fatality rates.
  • Countries like the US, France, Turkey, and China initially had high cCFR.
  • Germany, China, and South Korea showed low cCFR by April 22.
  • China and South Korea demonstrated effective epidemic control with high doubling times.
  • Russia's doubling time remained under 10 days.

Conclusions:

  • The cCFR method offers a more reliable measure of epidemic fatality.
  • Improved outcomes and wider testing strategies influence cCFR stability.
  • Effective epidemic control is linked to high doubling times and reduced cCFR.

Keywords:
Coronavirusepidemiologypublic health

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K

Related Concept Videos

Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.3K
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
320
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,...
223
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
1.5K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.1K
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...
7.5K