Methods for estimating the case fatality ratio for a novel, emerging infectious disease
Open accessAbstract
During the course of an epidemic of a potentially fatal disease, it is important that the case fatality ratio be well estimated. The authors propose a novel method for doing so based on the Kaplan-Meier survival procedure, jointly considering two outcomes (death and recovery), and evaluate its performance by using data from the 2003 epidemic of severe acute respiratory syndrome in Hong Kong, People's Republic of China. They compare this estimate obtained at various points in the epidemic with the case fatality ratio eventually observed; with two commonly quoted, naïve estimates derived from cumulative incidence and mortality statistics at single time points; and with estimates in which a parametric mixture model is used. They demonstrate the importance of patient characteristics regarding outcome by analyzing subgroups defined by age at admission to the hospital.
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The naive CFR approach (dividing deaths by cases) leads to biased estimates when the final status of some cases is unknown.
“Half of the estimates (n=57 estimates from 42 studies) were computed using a naive approach (ie, dividing the number of deaths by the number of cases), which leads to biased estimates when the final status of some cases is unknown.”
From Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review (2025) · cites this paper as [71]
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- Authors
- Ghani, A. C., Donnelly, C. A., Cox, D. R., Griffin, J. T., Fraser, C., Lam, T. H., Ho, L. M., Chan, W. S., Anderson, R. M., Hedley, A. J., & Leung, G. M.
- Affiliations
- Imperial College London (GB)
- University of Oxford (GB)
- University of Hong Kong (HK)
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Ghani, A. C.
- ORCID
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-09-01
- Volume
- 162
- Issue
- 5
- Pages
- 479-486
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC7109816
- OpenAlex
- W2109088393
- Cited by
- 252
- References
- 22
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 13 · 2023: 8 · 2022: 17 · 2021: 46 · 2020: 104 · 2019: 3 · 2017: 5 · 2016: 5 · 2015: 8 · 2014: 5 · 2013: 4 · 2012: 4
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Epidemiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (submittedVersion)
- Funders
- Boston College
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- case fatality rate, emerging infectious diseases, SARS, SARS epidemic, Hong Kong, Kaplan-Meier estimator, parametric mixture models, cumulative incidence, mortality statistics, subgroup analysis
- MeSH terms
- Female, Hong Kong, Humans, Male, Models, Statistical, Incidence, Survival Analysis, Communicable Diseases, Emerging, Severe Acute Respiratory Syndrome
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