The research is concerned with herd immunity of a population to an infectious disease, such as the current COVID-19 pandemic. The herd immunity level is defined as the fraction of the population that must become immune for disease spreading to decline and stop when all preventive measures, such as social distancing, are lifted. For COVID-19 it is often stated that this is around 60%, a figure derived from the fraction of the population that must be vaccinated (in advance of an epidemic) to prevent a large outbreak.  

The figure of 60% assumes that each individual in the population is equally likely to be vaccinated, and hence immune. However, that is not the case if immunity arises as a result of disease spreading in a population consisting of people with many different behaviours. The more socially active individuals are more likely to get infected than less socially active ones, and are also more likely to infect people if they become infected. Consequently the herd immunity level is lower when immunity is caused by disease spreading than when immunity comes from vaccination.  

In the paper, published in Science, the researchers investigate this using a simple model in which individuals are categorised into groups reflecting age and social activity level. When differences in age and social activity are incorporated in the model, the herd immunity level reduces from 60% to 43%. Further, the reduction in herd immunity level is mainly due to activity level rather than age structure. The figure of 43% should be interpreted as an illustration rather than an exact value or even a best estimate. 

The research is important as it has obvious consequences for the current COVID-19 pandemic and the release of lockdown and suggests that individual variation (e.g. in activity level) is an important feature to include in models that guide policy.


The article in Science: A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2