Estimates of the severity of coronavirus disease 2019: a model-based analysis

Robert Verity1, Lucy C Okell1, Ilaria Dorigatti1

  • 1MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics, and Department of Infectious Disease Epidemiology, Imperial College London, London, UK.

Insights

The case fatality ratio for coronavirus disease 2019 (COVID-19) varies significantly by age, with older individuals facing a substantially higher risk of death. These findings highlight the importance of age-specific risk assessment for COVID-19 mortality.

Area of Science:

  • Epidemiology
  • Infectious Disease Modeling

Background:

  • Estimates for coronavirus disease 2019 (COVID-19) case fatality ratio varied widely due to changing data and biases.
  • Robust estimates accounting for censoring and ascertainment biases were needed.

Purpose of the Study:

  • To provide robust estimates of the case fatality ratio (CFR) and infection fatality ratio (IFR) for COVID-19.
  • To analyze age-stratified risk of death and hospitalization for COVID-19.

Main Methods:

  • Collected individual-case data for COVID-19 deaths and recoveries in China and internationally.
  • Estimated time from symptom onset to outcome (death/discharge).
  • Adjusted CFR estimates for demography, under-ascertainment, and censoring; calculated IFR and hospitalization proportions.

Main Results:

  • Mean time from symptom onset to death was 17.8 days; to hospital discharge was 24.7 days.
  • Adjusted CFR in China was 1.38%, increasing significantly with age (6.4% for ≥60 years, 13.4% for ≥80 years).
  • IFR in China was 0.66%, also increasing with age; hospitalization risk reached 18.4% for those ≥80 years.

Conclusions:

  • COVID-19 fatality ratios exhibit a strong age gradient, with older individuals at higher risk.
  • Early estimates provide insight into the spectrum of COVID-19 severity and mortality risk across age groups.
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:
425
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:
819
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
406
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
200