Health

Predicting COVID-19 outbreaks using mobile device data


. But that process can slow down the actual transmission of the disease by days and weeks.

Key to the findings was the accuracy with which the researchers were able to identify incidents of high-frequency personal exposure (defined as a 6-foot radius) in Connecticut down to the city level.

The CDC advises people to stay at least 6 feet apart from others to avoid possible transmission of COVID-19.

The study’s lead author, Forrest Crawford, associate professor of biostatistics at the Yale School of Public Health, said: “Close person-to-person contact is the primary route of transmission of SARS-CoV-2, the virus that causes COVID-19. professor of ecology and evolutionary biology, management, statistics, and data science at Yale.

Researchers measured interpersonal contact within a 6-foot radius everywhere in Connecticut using mobile device geolocation data over the course of a year. The effort has given Connecticut epidemiologists and policymakers insight into people’s social distancing behavior across the state.

Other studies have used so-called “mobility metrics” as a proxy for socially distracting behavior and COVID-19 transmission. But that analysis may be flawed.

Mobility metrics typically measure the distance traveled or time taken away from a place, such as your home, but we all know it’s possible to move to many places and still not get close to others. .

Indicators of mobility are not a great proxy for transmission risk because a sense of close contact is better at predicting localized infection and outbreaks.

The discovery was based on a review of Connecticut mobile device geolocation data from February 2020 to January 2021. All data is anonymized and aggregated, and there is no identifying information. which individuals are collected.

A new algorithm calculated the probabilities of statewide close contact events (times when mobile devices are six feet apart) based on geolocation data.

That information is then incorporated into a standard COVID-19 transmission model to predict COVID-19 case levels not only across Connecticut but also in towns, census tracts, and regulatory blocks. individual census in Connecticut.

The exposure rates developed in this study may reveal high exposure conditions with potential for local outbreaks and high transmission risk residence areas days or weeks before the outbreaks. Cases are detected through testing, traditional case investigation, and contact tracing.

Source: Medindia



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