Demographic science aids in understanding the spread and fatality rates of COVID-19
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Abstract
Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.
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The disparity in COVID-19 burden between Africa and the rest of the world has been attributed to limited capacity for diagnosis, timely implementation of stringent containment measures, a younger population structure, and a predominance of asymptomatic and mild infections.
“This disparity has been attributed to limited capacity for diagnosis, timely implementation of stringent containment measures, a younger population structure, and a predominance of asymptomatic and mild infections (3, 4).”
From Seroprevalence of anti–SARS-CoV-2 IgG antibodies in Kenyan blood donors (2021) · cites this paper as [4]
The low COVID-19 mortality in Kenya can be partly explained by the steep demographic age-pyramid, with less than 4% of the population aged 65 or older.
“The low mortality can be partly explained by the steep demographics in Kenya, where less than 4% of the population is 65 or older.”
From Seroprevalence of anti–SARS-CoV-2 IgG antibodies in Kenyan blood donors (2021) · cites this paper as [4]
Simply counting total excess deaths does not provide an understanding of the substantial variation by age and sex over time in elevated mortality risks, nor does it allow for a comparison of current mortality conditions with past conditions due to changes in population age structure.
“While this metric provides an important measure of the burden of the pandemic on a society, simply counting total excess deaths does not provide an understanding of the substantial variation by age and sex over time in elevated mortality risks.10 11 nor does it allow for a comparison of current mortality conditions with past conditions due to changes in population age structure over the period.”
From Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis (2021) · cites this paper as [10]
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Self-citation
- Authors
- Dowd, J. B., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., Liu, Y., & Mills, M. C.
- Affiliations
- University of Oxford (GB)
- Princeton University (US)
- Corresponding author
- Dowd, Jennifer Beam
- Mills, Melinda C.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2020-04-16
- Volume
- 117
- Issue
- 18
- Pages
- 9696-9698
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC7211934
- OpenAlex
- W3016523961
- Cited by
- 905
- References
- 4
- Citations by year
- 2026: 14 · 2025: 22 · 2024: 53 · 2023: 79 · 2022: 139 · 2021: 270 · 2020: 328
- FWCI
- 32.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Keywords
- COVID-19 pandemic, age structure, age-specific mortality, fatality rate, population aging, disease transmission, social distancing, pandemic policy, hospitalization prediction, Italy, South Korea
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Adult, Age Factors, Aged, Aged, 80 and over, Female, Humans, Italy, Male, Middle Aged, Pneumonia, Viral, Sex Factors, Coronavirus Infections, Republic of Korea, Pandemics
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