Modelling COVID-19 Transmission

Access to population mobility data is giving researchers an important tool to use in modelling the spread and transmission of COVID-19. Their research, published in Nature, confirms that a small number of superspreader events cause a majority of illness.  

Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of ‘superspreader’ points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2,3,4,5,6,7,8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. 

Another article provides somewhat less technical context.  

With a year’s worth of data, researchers have amassed ample evidence of some chief ingredients of superspreading events: prolonged indoor gatherings with poor ventilation. Activities such as singing and aerobic exercise, which produce many of the tiny infectious droplets that can be inhaled by others, are also common components.

But key questions remain. “We have some ideas of what factors are involved, but we still don’t know what is the main driver of the superspreading,” says Endo. Foremost are uncertainties about how much individual differences in people’s behaviour and biology matter — or can be controlled — and how best to target high-risk settings while keeping the cogs of society turning. Understanding the underlying factors that drive superspreading is crucial, says Lucy Li, an infectious-diseases modeller at the Chan Zuckerberg Biohub in San Francisco, California.

Experts say that we already know enough about the main factors of superspreading to use this phenomenon to our advantage. They are calling on policymakers to harness this knowledge to target control measures that will slow — or even stamp out — the pandemic. One of the most basic steps is closing crowded, indoor hotspots to prevent superspreading events. Researchers also recommend following Japan’s lead, by using backwards contact tracing to uncover superspreading events.