Predicting The March Of COVID-19 From Mobile Phone Data

Using anonymized mobile phone data from nearly 11.5 million people, researchers could predict the spread of COVID-19 infections from Wuhan, China, up to two weeks in advance.

AsianScientist (Apr. 29, 2020) – Anonymized mobile phone data can be used to accurately predict the geographical and temporal spread of COVID-19 infections up to two weeks ahead of time, according to a study in Nature that tracked the movements of nearly 11.5 million people during the chunyun period of mass travel in China.

In the study, led by Nicholas Christakis at Yale University in the US and Jia Jianmin at The Chinese University of Hong Kong, the researchers analyzed the distribution of population outflows from Wuhan, China, during the early stages of the COVID-19 outbreak in January 2020.

Taking anonymized mobile phone data from a major national carrier in China, the researchers analyzed the movements of nearly 11.5 million people who spent at least two hours in Wuhan between January 1 and 24, 2020, when the quarantine was imposed. They linked these data to COVID-19 infection rates until February 19 from 296 prefectures in 31 provinces and regions throughout China.

The authors report that quarantine restrictions were highly effective at substantially reducing movement, with population outflows dropping by 52 percent from January 22 to 23, and by a further 94 percent on January 24. They also show that the distribution of population outflows could accurately predict the frequency and geographical locations of COVID-19 infections in China up to two weeks in advance.

“The logic of our population-flow-based ‘risk source’ model differs from classic epidemiological models that rely on assumptions regarding population mixing, population compartment sizes, and viral properties,” the authors write.

According to the authors, their model has several advantages, as it leverages population flow data to not only forecast confirmed cases, but to also identify potential high-transmission-risk cities at an early stage of the outbreak. It also makes no assumptions regarding travel patterns or effective distance effects, allows for non-linear estimations, and generates a non-arbitrary, source-linked risk score.

Importantly, the model can also accommodate multiple risk sources, for example in countries where there are multiple disease epicenters, they add. The findings could help policymakers in countries that have mobile phone data available to make rapid and accurate risk assessments and plan the allocation of limited resources during outbreaks, they conclude.

The article can be found at: Jia et al. (2020) Population Flow Drives Spatio-temporal Distribution of COVID-19 in China.


Source: Nature; Photo: Shutterstock.
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