According to the New York Times, there was a drop of 7.7 percentage points from the average people staying home in the United States during the peak period for sheltering in place. People started venturing outside their homes as more than half the states started to reopen their economies or were planning to do so. Social distancing is one of the most effective means to curb the spread of COVID-19. Therefore, this surge in mobility may indicate an upcoming new wave of infection. There are concerns among public health experts about the possibility of such an increase in cases.
This whole analysis is based on mobility data. It is powerful for some of the big picture insights on how people move and respond to containment strategies like social distancing or sheltering in place. The COVID-19 pandemic has substantially increased the use of data and machine learning models. The whole world is putting incredible efforts to fight against the virus in terms of data sources, infrastructure, etc. The researchers are focusing their expertise to develop mathematical models for analysing the situation using the mobility data. However, this increased mobility does not mean people are unsafe.
Researchers of the Network Science Institute at Northeastern University, provide an insight into how these models work. They tracked the co-location events, which means two mobile devices within a range of 60 feet of each other and a five-minute window. This will give some good numbers in terms of the number of contacts. However, it fails to tell what people are doing. These may be contacts in the sense of not violating the social norms.
All mobility increases are not equal. They should not be evaluated with the same parameters. Meeting a small number of people, wearing masks, and keeping a distance can help minimise the risks associated with moving out. Therefore, relying solely on numbers can be deceptive.