News Release

Modeling population differences influences the herd immunity threshold for COVID-19

Peer-Reviewed Publication

American Association for the Advancement of Science (AAAS)

A new modeling study illustrates how accounting for factors such as age and social activity influences the predicted herd immunity threshold for COVID-19, or the level of population immunity needed to stop the disease's transmission. The model hints that herd immunity could potentially be achieved with around 43% of the population being immune, as opposed to the 60% threshold derived from previous models. However, the authors stress that their study serves mostly as a sketch of how population differences affect herd immunity, rather than as a precise estimate. As Science Editor-in-Chief Holden Thorp notes in a related Science Editor's blog post in which he discusses how the journal weighed potential costs and benefits of publishing this study, "Even if the model's most optimistic prediction of 43% as a herd immunity threshold is correct, none of the seroprevalence studies that we are aware of suggest that any country is close to achieving herd immunity. Continuing non-pharmaceutical interventions around the world is still of great importance." As health authorities grapple with how to respond to the COVID-19 pandemic and whether to lift restrictions, some have expressed fear that lifting restrictions before herd immunity is achieved could lead to a second wave of infections. Here, Tom Britton and colleagues simulated herd immunity using an epidemiological model that accounts for the influence of age and social activity on a person's susceptibility to COVID-19, in contrast to simpler models where all population members are equally susceptible to infection. Assuming that an infected person transmitted the virus to an average of 2.5 other people, their model predicted that a herd immunity level of 43% was sufficient to prevent a second major outbreak after lifting restrictions. Britton et al. call for further studies with more complex models, but speculate that lifting social restrictions gradually, rather than simultaneously, could help prevent a resurgence of infections.


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