The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2
Open accessAbstract
Heterogeneity and herd immunity In response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), some politicians have been keen to exploit the idea of achieving herd immunity. Countering this possibility are estimates derived from work on historical vaccination studies, which suggest that herd immunity may only be achieved at an unacceptable cost of lives. Because human populations are far from homogeneous, Britton et al. show that by introducing age and activity heterogeneities into population models for SARS-CoV-2, herd immunity can be achieved at a population-wide infection rate of ∼40%, considerably lower than previous estimates. This shift is because transmission and immunity are concentrated among the most active members of a population, who are often younger and less vulnerable. If nonpharmaceutical interventions are very strict, no herd immunity is achieved, and infections will then resurge if they are eased too quickly. Science , this issue p. 846
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“The disease and death that would accompany natural infection rates to reach herd immunity, typically estimated as 40–60% of the population infected, would be unacceptably high.”
From Lancet COVID-19 Commission Statement on the occasion of the 75th session of the UN General Assembly (2020) · cites this paper as [30]
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“The latest estimates on COVID-19, pointed out a range of 60–75% immune individuals that would be necessary to halt the forward transmission of the virus and community spread of the virus [87,88,89].”
From COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates (2021) · cites this paper as [89]
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Self-citation
Research works printed but not linked: 2
- F. Ball T. Britton P. Trapman Code for: Population heterogeneity and consequences for herd immunity to SARS-CoV-2 Version 1 Zenodo (2020); https://doi.org/10.1101/10.5281/zenodo.3899252. · DOI
- S. N. Ethier T. G. Kurtz Markov Processes: Characterization and Convergence (Wiley 2009).
Cited by
Sorted by citations
COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates
Sallam, M. (2021) · Vaccines · doi:10.3390/vaccines9020160 · Cited by 2,128
Sherman, S. M., Smith, L., Sim, J., Amlôt, R., Cutts, M., Dasch, H. K., Rubin, G. J., & Sevdalis, N. (2020) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2020.1846397 · Cited by 748
Infection fatality rate of COVID-19 inferred from seroprevalence data
Ioannidis, J. P. A. (2021) · Bulletin of the World Health Organization · doi:10.2471/blt.20.265892 · Cited by 684
COVID-19 herd immunity: where are we?
Fontanet, A. L., & Cauchemez, S. (2020) · Nature reviews. Immunology · doi:10.1038/s41577-020-00451-5 · Cited by 552
Metadata
- Authors
- Britton, T., Ball, F., & Trapman, P.
- Affiliations
- Stockholm University (SE)
- University of Nottingham (GB)
- Corresponding author
- Britton, Tom
- Venue
- Science
- Published
- 2020-08-14
- Volume
- 369
- Issue
- 6505
- Pages
- 846-849
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7331793
- OpenAlex
- W3036932834
- Cited by
- 645
- References
- 15
- Citations by year
- 2026: 24 · 2025: 38 · 2024: 48 · 2023: 58 · 2022: 114 · 2021: 255 · 2020: 107
- FWCI
- 21.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository@Nottingham (University of Nottingham) (publishedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Funders
- Vetenskapsrådet (2016-04566)
- The swedish Research Council (2015-05015)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- herd immunity, SARS-CoV-2, population heterogeneity, age heterogeneity, transmission dynamics, mathematical model, non-pharmaceutical interventions, infection rate, COVID-19 pandemic
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Age Factors, Demography, Humans, Models, Theoretical, Pneumonia, Viral, Social Behavior, Coronavirus Infections, Immunity, Herd, Basic Reproduction Number, Pandemics, Social Participation
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