Related Experiment Video
Updated: Dec 17, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2
Tom Britton1, Frank Ball2, Pieter Trapman3
1Department of Mathematics, Stockholm University, Stockholm, Sweden. tom.britton@math.su.se.
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.
More Related Videos
06:08Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research
Published on: September 8, 2023
09:09Generating a Reproducible Model of Mid-Gestational Maternal Immune Activation using PolyI:C to Study Susceptibility and Resilience in Offspring
Published on: August 17, 2022
Related Concept Videos
Population Growth
Mechanistic Models: Compartment Models in Individual and Population Analysis
Analysis of Population Pharmacokinetic Data
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the...
Distributions to Estimate Population Parameter
Exponential Equations for Modeling Growth