Researchers use mobile device data to predict COVID-19 outbreaks
Date:
January 31, 2022
Source:
Yale School of Public Health
Summary:
Researchers were able to accurately predict outbreaks of COVID-19 in
Connecticut municipalities using anonymous location information from
mobile devices, according to a new study. The novel analysis applied
in the study could help health officials stem community outbreaks
of COVID- 19 and allocate testing resources more efficiently,
the researchers said.
FULL STORY ========================================================================== Researchers at the Yale School of Public Health were able to accurately
predict outbreaks of COVID-19 in Connecticut municipalities using
anonymous location information from mobile devices, according to a new
study published in Science Advances.
==========================================================================
The novel analysis applied in the study could help health officials
stem community outbreaks of COVID-19 and allocate testing resources more efficiently, the researchers said.
The study was conducted by data scientists and epidemiologists from the
Yale School of Public Health, the Connecticut Department of Public Health,
the U.S.
Centers for Disease Control and Prevention and Whitespace Ltd., a spatial
data analytics firm.
The key to the findings was the precision with which 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 the study's lead
author Forrest Crawford, an associate professor of biostatistics at the
Yale School of Public Health and an associate professor of ecology and evolutionary biology, management, statistics and data science at Yale.
"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," Crawford said. "This effort gave Connecticut epidemiologists and policymakers insight to people's social distancing
behavior statewide." 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.
========================================================================== "Mobility metrics often measure distance traveled or time spent away from
a location, such as your home," Crawford said. "But we all know it's
possible to move around a lot and still not get very close to other
people. So mobility metrics are not a great proxy for transmission
risk. We feel close contact predicts infections and local outbreaks
better." The findings are based on a review of Connecticut mobile device geolocation data from February 2020 to January 2021. All of the data was anonymized and aggregated, and no personally identifiable information
was collected.
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. That information was then incorporated into
a standard COVID- 19 transmission model to predict COVID-19 case levels
not only across Connecticut, but in individual Connecticut towns, census tracts, and census block groups.
The researchers said they successfully predicted an initial wave of
Connecticut COVID-19 cases from March to April 2020, a drop in statewide
cases during June to August and localized outbreaks in certain Connecticut towns in August and September.
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-19. But that process can lag actual disease transmission
by days and weeks. Analyzing close personal contact rates is much faster,
the researchers said.
"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.
special promotion Explore the latest scientific research on sleep and
dreams in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided by Yale_School_of_Public_Health. Original written by Colin Poitras. Note: Content may be edited for style and
length.
========================================================================== Journal Reference:
1. Forrest W. Crawford, Sydney A. Jones, Matthew Cartter, Samantha
G. Dean,
Joshua L. Warren, Zehang Richard Li, Jacqueline Barbieri, Jared
Campbell, Patrick Kenney, Thomas Valleau, Olga Morozova. Impact of
close interpersonal contact on COVID-19 incidence: Evidence from
1 year of mobile device data. Science Advances, 2022; 8 (1) DOI:
10.1126/ sciadv.abi5499 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/01/220131083839.htm
--- up 8 weeks, 2 days, 7 hours, 13 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)