Infection fatality ratio and case fatality ratio of COVID-19
Open accessAbstract
The infection fatality ratio (IFR) is the risk of death per infection and is one of the most important epidemiological parameters. Enormous efforts have been undertaken to estimate the IFR for COVID-19. This study examined the pros and cons of several approaches. It is found that the frequently used approaches using serological survey results as the denominator and the number of confirmed deaths as the numerator underestimated the true IFR. The most typical examples are South Africa and Peru (before official correction), where the confirmed deaths are one-third of the excess deaths. We argue that the RT-PCR-based case fatality ratio (CFR) is a reliable indicator of the lethality of COVID-19 in locations where testing is extensive. An accurate IFR is crucial for policymaking and public-risk perception.
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Published estimates of CFR suggest high severity, with more than 10% of cases being fatal, which is one order of magnitude higher than that estimated for SARS-CoV-2 (CFR up to approximately 2% for the most severe variants) or pandemic influenza virus (CFR up to approximately 1-3% for the 1918 influenza pandemic).
“Published estimates of CFR suggest high severity, with more than 10% of cases being fatal, which is one order of magnitude higher than that estimated for SARS-CoV-2 (CFR up to approximately 2% for the most severe variants 112,119 ) or pandemic influenza virus (CFR up to approximately 1-3% for the 1918 influenza pandemic 120 ).”
From Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review (2025) · cites this paper as [119]
The Results for selected RT-PCR data report COVID-19 case-fatality estimates reaching about 2% in selected locations. The paper does not attribute that upper value to the most severe SARS-CoV-2 variants; its estimates are bounded by location and testing conditions, not variant severity. The citation is attached to the SARS-CoV-2 comparison, not the 1918 influenza estimate. (LLM, conf=0.86)
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References 16 · Cited by 36
Research works printed but not linked: 1
- Levin Andrew T. Hanage William P. Owusu-Boaitey Nana Cochran Kensington B. Walsh Seamus P. Meyerowitz-Katz Gideon Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications European journal of epidemiology 35 2020 1123 1138 33289900 PMC7721859
- Authors
- Luo, G., Zhang, X., Zheng, H., & He, D.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Shaanxi Normal University (CN)
- Corresponding author
- Luo, Guangze
- Zhang, Xingyue
- Zheng, Hua
- He, Daihai
- Venue
- International Journal of Infectious Diseases
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 113
- Pages
- 43-46
- ISSN
- 1201-9712 (ISSN-L)
- PMC
- PMC8496974
- OpenAlex
- W3205738665
- Semantic Scholar
- c78c643a5803134a652ff205c1c7c7b8c46d118c
- Cited by
- 36
- References
- 16
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 12 · 2022: 17
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Infectious Diseases (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (11905120, 11947416)
- Research Grants Council, University Grants Committee (HKU C7123-20G)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- COVID-19 pandemic, infection fatality rate, case fatality rate, excess mortality, serological survey, RT-PCR testing, mortality underestimation, South Africa, Peru, public health policy
- MeSH terms
- COVID-19, SARS-CoV-2, Humans, Peru, South Africa
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