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Modeling herd immunity

Today, Science published a study by Britton et al. that incorporates population heterogeneity into modeling the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—that is, it accounts for the fact that members of a population may have differential exposure and susceptibility to infection. In this model, the authors vary the extent of mobility and the susceptibility to infection by age. These heterogeneities lead to a decrease in the percentage of the population that must be infected to achieve herd immunity, the condition in which a threshold proportion of immune individuals should lead to a decline in the incidence of infection. The authors are very careful to point out that this is just a model that is highly dependent on their assumptions.

The prevailing assumption in the public discourse is that herd immunity will require 60% of the population to be infected. This estimate is based on the reproductive number (R0), which is the number of cases, on average, that an infected person will cause during their infection period. It is calculated as 1 – 1/R0, and for SARS-CoV-2, the authors use a value of R0 = 2.5. Using the parameters that Britton et al. put in their model, the effective herd immunity threshold is reduced to 43%. The last sentence of the abstract is worth emphasizing: “Our estimates should be interpreted as an illustration of how population heterogeneity affects herd immunity, rather than an exact value or even a best estimate.”

The relevant Science editors discussed whether it was in the public interest to publish the findings. Like all Science papers, the article received support from members of our Board of Reviewing Editors and experts who provided peer review. Nevertheless, we were concerned that forces that want to downplay the severity of the pandemic as well as the need for social distancing would seize on the results to suggest that the situation was less urgent. We decided that the benefit of providing the model to the scientific community was worthwhile. The effects of many variabilities on infection spread, including age, genetics, and past exposures to other vaccines and viruses, are beginning to emerge for SARS-CoV-2. Together with behavior, these factors will affect the degree to which different populations are susceptible to infection, and understanding population heterogeneity may guide vaccination strategies.

Even if the model’s most optimistic prediction of 43% as a herd immunity threshold is correct, none of the seroprevalence studies (assessing the number of persons in a population who test positive for SARS-CoV-2 based on the presence of specific antibodies) that we are aware of suggest that any country is close to achieving herd immunity. Continuing nonpharmaceutical interventions around the world is still of great importance.

Holden Thorp is the Editor-in-Chief of the Science family of journals.

Valda Vinson is the Editor of research for Science.

Caroline Ash is a Senior Editor for Science.