Superspreading in SARS-CoV-2 – an evolving property
As part of the on-going partnership between PandemiX and the Niels Bohr Institute at University of Copenhagen, researchers have studied the evolutionary aspects of superspreading. In a recent study in Epidemics, they find that superspreading is not a static property of the virus, but rather one that evolves over time. Using a mathematical model, they go on to show that interventions such as lockdowns may alter the evolutionary course of the trait.
During the COVID-19 pandemic, superspreading has been a significant contributing factor to the overall spread of the disease. In fact, with the original strain of the virus, transmission was so uneven that 10% of infected people caused around 80% of new infections. As such, superspreading has been one of the signature features of the pandemic. However, the authors of the Epidemics study note that there are signs that transmission is becoming less uneven over time.
At first, this may seem surprising. However, building on their earlier work on mathematical modelling of superspreading, they were able to show that this was in fact to be expected: less ‘superspread-y’ variants have a natural advantage, since their transmission chains are much less likely to die out.
Even more intriguingly, the evolution of the superspreading trait doesn’t just happen. The mathematical model suggests that public health interventions such as lockdowns may accelerate this process, since superspreading variants are much more vulnerable to such interventions than their more evenly spreading counterparts.
These findings have implications for how best to handle future variants of SARS-CoV-2, as well as for future pandemics in general. Superspreading strongly affects the effectiveness of public health interventions, so any predictable evolutionary changes in this statistical trait are of great importance.
More generally, the new Epidemics study highlights the importance of building a strong understanding of the interplay between behaviour (including interventions) and pathogen evolution. In other words: How do our actions affect which pathogens will evolve?