New outbreaks of coronavirus have learned to predict according to data from a smartphone

Scientists at the Yale School of Public Health found Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data a new way to predict areas where the likelihood of mass infection of COVID-19 is especially high. This will help allocate the resources of medical institutions more efficiently and plan patient testing.

The researchers took into account that the coronavirus spreads fastest where people come into contact with each other especially often and closely. They set a critical distance — 182 cm: if a significant proportion of interactions occur at this or less distance, the likelihood of infection increases.

In the work, the researchers used anonymous geolocation data of users from their mobile devices. They calculated the probability of a large number of close contacts in different areas. This information was then added to a standard mathematical model of COVID-19 transmission and the incidence rate was determined.

The researchers stressed that their technique made it possible to successfully predict the first wave of coronavirus infection in Connecticut in March-April 2020, as well as a decrease in the number of cases here in June-August and local outbreaks of COVID-19 in individual cities state. The prognosis is quite accurate: it reveals spikes in infection days or weeks before the first symptoms and test results appear.

Scientists hope that their forecasting technique will be implemented in other regions and will help to avoid mass outbreaks in the future. The forecast will also help prepare hospitals for mass testing and admission of patients.