Heterogeneity and superspreading effect on herd immunity

Yaron Oz*, Ittai Rubinstein, Muli Safra

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We model and calculate the fraction of infected population necessary to reach herd immunity, taking into account the heterogeneity in infectiousness and susceptibility, as well as the correlation between those two parameters. We show that these cause the effective reproduction number to decrease more rapidly, and consequently have a drastic effect on the estimate of the necessary percentage of the population that has to contract the disease for herd immunity to be reached. We quantify the difference between the size of the infected population when the effective reproduction number decreases below 1 vs the ultimate fraction of population that had contracted the disease. This sheds light on an important distinction between herd immunity and the end of the disease and highlights the importance of limiting the spread of the disease even if we plan to naturally reach herd immunity. We analyze the effect of various lock-down scenarios on the resulting final fraction of infected population. We discuss implications to COVID-19 and other pandemics and compare our theoretical results to population-based simulations. We consider the dependence of the disease spread on the architecture of the infectiousness graph and analyze different graph architectures and the limitations of the graph models.

Original languageEnglish
Article number033405
JournalJournal of Statistical Mechanics: Theory and Experiment
Volume2021
Issue number3
DOIs
StatePublished - Mar 2021

Funding

FundersFunder number
Horizon 2020 Framework Programme835152

    Keywords

    • epidemic modeling
    • network dynamics
    • population dynamics
    • stochastic processes

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