ArticlePhysical biology2020
Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them.
Article in Physical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- A Bayesian estimator of the real-time case fatality rate in emerging pandemic to inform public health policy-making.BMC infectious diseases · 2026Article
- Aggregating multiple test results to improve medical decision-making.PLoS computational biology · 2025Article
- Forecasting drug-overdose mortality by age in the United States at the national and county levels.PNAS nexus · 2024Article
- A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity.Frontiers in big data · 2023Article
- Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19.Scientific reports · 2022Article
- A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- COVID-19 severity determinants inferred through ecological and epidemiological modeling.One health (Amsterdam, Netherlands) · 2021Article
- Decisive Conditions for Strategic Vaccination against SARS-CoV-2.medRxiv : the preprint server for health sciences · 2021Article
- A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors.medRxiv : the preprint server for health sciences · 2021Article
- Using excess deaths and testing statistics to determine COVID-19 mortalities.European journal of epidemiology · 2021Article
- Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities.medRxiv : the preprint server for health sciences · 2021Article
- Using excess deaths and testing statistics to improve estimates of COVID-19 mortalities.ArXiv · 2021Article
- Cancer Treatment and Research During the COVID-19 Pandemic: Experience of the First 6 Months.Oncology and therapy · 2020Article
- An evaluation of mathematical models for the outbreak of COVID-19.Precision clinical medicine · 2020Article
- Global prediction of unreported SARS-CoV2 infection from observed COVID-19 cases.medRxiv : the preprint server for health sciences · 2020Article
- Africa's COVID-19 Situation in Focus and Recent Happenings: A Mini Review.Frontiers in public health · 2020Review
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3 authors.
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Abstract
Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the 'CFR' has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is the case fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual's probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data arising from, e.g., undertesting and heterogeneous populations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.