Mobile Device Data can Predict Covid-19 Outbreaks

Covid-19 outbreaks can be predicted using anonymous location information from mobile devices and analyzing close personal contact rates is much faster.

Covid-19 outbreaks have been predicted accurately by US researchers using anonymous location information from mobile devices.

The novel analysis applied in the study, led by a team at the Yale School of Public Health, could help health officials stem community outbreaks of Covid-19 and allocate testing resources more efficiently, researchers said.

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‘Analyzing close personal contact rates is much faster than any other way of collecting data on Covid-19 outbreaks.’

In the study published in the journal Science Advances, the researchers were able to identify incidents of high frequency close personal contact (defined as a radius of 6 feet) in Connecticut down to the municipal level.

The CDC advises people to keep at least six feet of distance with others to avoid possible transmission of Covid-19.

"Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes Covid-19," said lead author Forrest Crawford, an associate professor of biostatistics at Yale.

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"The contact rate we developed in this study can reveal high-contact conditions likely to spawn local outbreaks and areas where residents are at high transmission risk days or weeks before the resulting cases are detected through testing, traditional case investigations and contact tracing," Crawford said.

The findings are based on a review of Connecticut mobile device geolocation data from February 2020 to January 2021.

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A novel algorithm computed the probability of close contact events across the state (times when mobile devices were within six feet of each other) based on geolocation data.

"We measured close interpersonal contact within a 6-foot radius everywhere in Connecticut using mobile device geolocation data over the course of an entire year.

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"This effort gave Connecticut epidemiologists and policymakers insight to people's social distancing behavior statewide," Crawford said.

Other studies have used so-called "mobility metrics" as proxy measures for social distancing behavior and potential Covid-19 transmission. But that analysis can be flawed.

Many health officials currently rely on general surveillance data such as the number of confirmed cases, hospitalizations and deaths to track the spread of Covid. But that process can lag actual disease transmission by days and weeks. Analyzing close personal contact rates is much faster, the researchers said.

Source-IANS