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Research Article
Jianmin Jia
Shenzhen Finance Institute, School of Management and Economics, The Chinese University of Hong Kong; Shenzhen Institute of Artificial Intelligence and Robotics for Society
Corresponding Author
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Sudden, large-scale, and diffuse human migration can amplify localized outbreaks into pandemics. Rapid and accurate tracking of human population mobility using reliable mobile phone data could therefore be helpful for policy responses. Here, we examine mobile phone geolocation data for all 18,514,920 counts of individuals egressing or transiting through the prefecture of Wuhan, China, between January 1 and 24, 2020. First, we document the efficacy of quarantine measures in ceasing population movement.
Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency of, and geographical distribution of, COVID-19 infections through February 12, 2020, across all of China, up to two weeks in advance. Third, we present a risk model that not only predicts confirmed cases, but also identifies high- transmission-risk locales at an early stage. Fourth, we develop a mobility-data-driven modeling framework to statistically derive the growth pattern of COVID-19 and its spread; this model can yield a benchmark trend and an index for assessing COVID-19 risk for different geographic locations (290 prefectures). Prefectures above the index’s 90% confidence interval are likely experiencing more local transmissions than imported cases; prefectures below the 90% confidence interval are controlling the spread of the virus more effectively (or, alternatively, are at greater risk of information inaccuracy). This approach can be used by policy makers in developing nations, which typically have mobile phone infrastructure but limited healthcare capabilities, to make rapid and accurate risk assessments ahead of second-wave outbreaks in order to overcome resource and logistics limitations.
Keywords
human mobility data, COVID-19, geographic distribution, population-outflow risk model
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See the published version of this preprint at Nature Portfolio.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Jianmin Jia
Shenzhen Finance Institute, School of Management and Economics, The Chinese University of Hong Kong; Shenzhen Institute of Artificial Intelligence and Robotics for Society
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Nature Portfolio.
Version 1
Posted 12 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epidemiology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Nature Portfolio.
Sudden, large-scale, and diffuse human migration can amplify localized outbreaks into pandemics. Rapid and accurate tracking of human population mobility using reliable mobile phone data could therefore be helpful for policy responses. Here, we examine mobile phone geolocation data for all 18,514,920 counts of individuals egressing or transiting through the prefecture of Wuhan, China, between January 1 and 24, 2020. First, we document the efficacy of quarantine measures in ceasing population movement.
Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency of, and geographical distribution of, COVID-19 infections through February 12, 2020, across all of China, up to two weeks in advance. Third, we present a risk model that not only predicts confirmed cases, but also identifies high- transmission-risk locales at an early stage. Fourth, we develop a mobility-data-driven modeling framework to statistically derive the growth pattern of COVID-19 and its spread; this model can yield a benchmark trend and an index for assessing COVID-19 risk for different geographic locations (290 prefectures). Prefectures above the index’s 90% confidence interval are likely experiencing more local transmissions than imported cases; prefectures below the 90% confidence interval are controlling the spread of the virus more effectively (or, alternatively, are at greater risk of information inaccuracy). This approach can be used by policy makers in developing nations, which typically have mobile phone infrastructure but limited healthcare capabilities, to make rapid and accurate risk assessments ahead of second-wave outbreaks in order to overcome resource and logistics limitations.
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