Science. 2020 Aug 14;369(6505):846-849. doi: 10.1126/science.abc6810. | PubMed

Tom Britton¹, Frank Ball², Pieter Trapman³

1. Department of Mathematics, Stockholm University, Stockholm, Sweden. tom.britton@math.su.se.
2. School of Mathematical Sciences, University of Nottingham, Nottingham, UK.
3. Department of Mathematics, Stockholm University, Stockholm, Sweden.

Abstract

Despite various levels of preventive measures, in 2020, many countries have suffered severely from the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Using a model, we show that population heterogeneity can affect disease-induced immunity considerably because the proportion of infected individuals in groups with the highest contact rates is greater than that in groups with low contact rates. We estimate that if R 0 = 2.5 in an age-structured community with mixing rates fitted to social activity, then the disease-induced herd immunity level can be ~43%, which is substantially less than the classical herd immunity level of 60% obtained through homogeneous immunization of the population. Our estimates should be interpreted as an illustration of how population heterogeneity affects herd immunity rather than as an exact value or even a best estimate.

Presented By Tom Britton | ORCID iD