&Bullet; physics 14, p22
A new model shows that limiting the number of social interactions between members of a population is effective in controlling outbreaks dominated by “superspreaders”, which explains the unexpected success of the bans last year.
In early 2020, many countries put in lockdowns to contain the spread of COVID-19. At the time, models indicated that these lockdowns would only have a moderate impact on the number of infections. But the strategy turned out to be surprisingly effective, says Kim Sneppen, a physicist who studies epidemics at the University of Copenhagen in Denmark. To understand the reasons for this unexpected result, Sneppen and his colleagues took another look at their models. Their results, released today, show that the discrepancy can be explained by the heterogeneity of infection rates – something that was not considered a year ago but is now known to be characteristic of COVID-19  . Taking this heterogeneity into account leads to a sharp increase in the predicted effectiveness of mitigation strategies that limit the social interactions of a population.
The spread of the flu is relatively easy to measure with a standard epidemic model, since its infection rate – the number of people infected by each infected person – hardly varies from person to person. At first, epidemiologists thought that the infection rate of COVID-19, while higher than that of the flu, was just as uniform. However, during 2020 it became clear that this was not the case. Instead, some COVID-19 carriers do not infect anyone, while others, so-called super spreaders, infect entire crowds.
The team incorporated such a variable rate of infection into an agent-based COVID-19 model that treats the population as a collection of autonomous decision-makers. They then used the model to test the effectiveness of various mitigation strategies. They found that limiting the number of social interactions between groups of people – something that happens during lockdown – rather than their duration was the best way to stop the disease from spreading. The same containment strategy wouldn’t work with flu.
Katherine Wright is assistant editor of physics.
- BF Nielsen et al., “COVID-19 superspreading suggests mitigation through modulation of social networks”, Phys. Rev. Lett.126, 118301 (2021).