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Cited by 645COVID-19 epidemiological studiesMathematical and Theoretical Epidemiology and Ecology ModelsSARS-CoV-2 and COVID-19 Research

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

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Britton, Tom, Ball, Frank, Trapman, Pieter·2020·Science
DOI 10.1126/science.abc6810PMC PMC7331793
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Abstract

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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Fig. 1 Overall fraction infected over time. Shown is a plot of the overall fraction infected over time for the age and activity structured community with R0 = 2.5 for four different preventive levels inserted 15 March (day 30) and lifted 30 June (day 135). The blue, red, yellow, and purple curves correspond to no, light, moderate, and severe preventive measures, respectively.
Fig. 1 Overall fraction infected over time. Shown is a plot of the overall fraction infected over time for the age and activity structured community with R0 = 2.5 for four different preventive levels inserted 15 March (day 30) and lifted 30 June (day 135). The blue, red, yellow, and purple curves correspond to no, light, moderate, and severe preventive measures, respectively.CC BY · PubMed Central
Fig. 2 Cumulative fraction infected over time. Shown is a plot of the cumulative fraction infected over time for the age and activity structured community and R0 = 2.5 for four different preventive levels inserted 15 March (day 30) and lifted 30 June (day 135). The blue, red, yellow, and purple curves correspond to no, light, moderate, and severe preventive measures, respectively.
Fig. 2 Cumulative fraction infected over time. Shown is a plot of the cumulative fraction infected over time for the age and activity structured community and R0 = 2.5 for four different preventive levels inserted 15 March (day 30) and lifted 30 June (day 135). The blue, red, yellow, and purple curves correspond to no, light, moderate, and severe preventive measures, respectively.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. 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
  2. S. N. Ethier T. G. Kurtz Markov Processes: Characterization and Convergence (Wiley 2009).

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Metadata

Authors
Britton, T., Ball, F., & Trapman, P.
Affiliations
  • Stockholm University (SE)
  • University of Nottingham (GB)
Corresponding author
Britton, Tom
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2020-08-14
Volume
369
Issue
6505
Pages
846-849
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.abc6810
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
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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