Tracking the aggregated movements of people using their mobile phone data may help predict the geographical and temporal spread of COVID-19 infections up to two weeks ahead of time, according to a study.
The research, published in the journal Nature, analysed the distribution of population outflows from Wuhan, China, during the early stages of the COVID-19 outbreak in January 2020.
According to the scientists, including Nicholas Christakis, from Yale University in the US, large-scale population movements can contribute to localised outbreaks of a disease becoming widespread epidemics.
In the study, they assessed anonymised mobile phone data from a major national carrier in China to analyse the movements of more than 11 million people who spent at least 2 hours in Wuhan between 1 and 24 January 2020, when the quarantine was imposed.
The researchers linked the data to COVID-19 infection rates until 19 February from 296 prefectures in 31 provinces and regions throughout China.
According to the study, quarantine restrictions were highly effective at substantially reducing movement, with population outflows dropping by 52 per cent from 22 January to 23 January, and by a further 94 per cent on 24 January.
They also showed 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 model could also identify potential high-transmission-risk cities at an early stage of the outbreak, the scientists said, adding that it could be used to assess COVID-19 community transmission risk over time in different locations in the future.
Using the method, the researchers said policymakers in other countries which have mobile phone data available could make rapid and accurate risk assessments, and plan the allocation of limited resources during outbreaks.
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