In cities worldwide, coronavirus outbreaks have been linked to restaurants, cafes and gyms. Now, a new model using mobile-phone data to map people’s movements suggests that these venues could account for most COVID-19 infections in US cities.
The model, published in Nature today, also reveals how reducing occupancy in venues can significantly cut the number of infections.
The model “has concrete pointers as to what may be cost-effective measures to contain the spread of the disease, while at the same time, limiting the damage to the economy”, says Thiemo Fetzer, an economist at the University of Warwick in Coventry. “This is the policy sweet spot.”
https://www.nature.com/articles/d41586-020-03140-4The mobility data also suggest why people from poorer neighbourhoods are more likely to get COVID-19: because they are less able to work from home, and the stores they visit for essential supplies are often more crowded than in other areas. The average grocery store in poorer neighbourhoods had 59% more hourly visitors per square foot, and visitors stayed on average 17% longer than at stores outside those areas. Leskovec says that people living in these areas probably have limited options to visit less crowded stores, and as a result, a shopping trip is twice as risky as it is for someone from a wealthier area.
But Christopher Dye, an epidemiologist at the University of Oxford, says these mobility patterns need to be validated with real-world data. “It is an epidemiological hypothesis that remains to be tested. But it is a hypothesis that is well worth testing,” he says.
