Mobile phone location data tallies with Covid-19 infection rates, study finds
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Researchers have tied a decline in workplace mobile phone activity with a significantly lower rate of Covid-19 infections, putting some statistical clout behind the effectiveness of stay-at-home orders.
The team from the University of Pennsylvania believe the patterns they saw in publicly available mobile phone location data could be used to better estimate growth rates for Covid-19 in the future and inform decision making about when it is appropriate to instigate localised shutdowns.
“It is our hope that counties might be able to incorporate these publicly available mobile phone data to help guide policies regarding re-opening throughout different stages of the pandemic,” said the study’s senior author, Joshua Baker.
“Further, this analysis supports the incorporation of anonymised mobile phone location data into modelling strategies to predict at-risk counties across the US before outbreaks become too great.”
The team used location data from mobile phones which were de-identified and made publicly available by Google to analyse activity across up to 2,740 counties in the United States between early January and early May 2020.
This data was broken up into locations where the activity took place, ranging from workplaces, to homes, retail stores, grocery stores, parks, and transit stations. Roughly between 22,000 and 84,000 points of data were analysed for each day in the study period.
The idea was to compare where mobile phone activity took place as a proxy to show where people spent their time. This data was compared between two time periods: the first in January and February, before Covid-19’s outbreak in the United States, then mid-February through early May, during the virus’ initial surges and when stay-at-home orders were enacted.
Intuitively, they noted an increase in time spent at home, while visits to the workplace dropped significantly, along with a decline in visits to retail locations (such as stores and restaurants) and transit stations.
They saw that in counties where there was initially a higher density of cases, visits to workplaces, as well as retail locations and transit stations, fell more sharply than counties less affected by the virus. At the same time, in these counties, there was a more prominent spike in activity at homes.
In addition, the researchers saw that the counties where workplace activity fell the most had the lowest rates of new Covid-19 cases in the days that followed. Lag-times of 5, 10 and 15 days were observed to allow time for Covid-19’s incubation period, but the lower infection rates held across the range.
The team hopes that mobile phone data can be used effectively in the future to help predict Covid-19 hotspots and guide decision making.
“It will be important to confirm that cell phone data is useful in other stages of the pandemic beyond initial containment,” Baker said. “For example, is monitoring these data helpful during the reopening phases of the pandemic or during an outbreak?”
“They do have the potential to help us better understand behavioural patterns which could help future investigators predict the course of future epidemics or perhaps monitor the impact of different public health measures on peoples’ behaviours.”
In March, the UK Government asked mobile operators to provide information on their users’ movements and data usage patterns in order to gain a better understanding of the extent to which its lockdown measures are being adhered.
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