Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold.

COVID-19 Epidemic model Frailty variation Herd immunity threshold Individual variation Selection within cohorts

Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
14 Feb 2022
Historique:
pubmed: 9 6 2020
medline: 9 6 2020
entrez: 9 6 2020
Statut: epublish

Résumé

Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being critical to protect vulnerable individuals from severe outcomes as the virus becomes endemic.

Identifiants

pubmed: 32511451
doi: 10.1101/2020.04.27.20081893
pmc: PMC7239079
pii:
doi:

Types de publication

Preprint

Langues

eng

Commentaires et corrections

Type : UpdateIn

Auteurs

M Gabriela M Gomes (MGM)

Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.
Centro de Matemática e Aplicações, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal.

Marcelo U Ferreira (MU)

Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, Nova University of Lisbon, Lisbon, Portugal.

Rodrigo M Corder (RM)

Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.

Jessica G King (JG)

Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK.

Caetano Souto-Maior (C)

Laboratory of Systems Genetics, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.

Carlos Penha-Gonçalves (C)

Instituto Gulbenkian de Ciência, Oeiras, Portugal.

Guilherme Gonçalves (G)

Unidade Multidisciplinar de Investigação Biomédica, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal.

Maria Chikina (M)

Department of Computational and Systems Biology, University of Pittsburgh, Pittburgh, PA, USA.

Wesley Pegden (W)

Department of Mathematical Sciences, Carnegie Mellon University, , Pittburgh" , PA, USA.

Ricardo Aguas (R)

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Classifications MeSH