北京
爆发
移动电话
2019年冠状病毒病(COVID-19)
地理
中国
电话
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
人口学
入射(几何)
环境卫生
医学
计算机科学
传染病(医学专业)
病毒学
电信
疾病
数学
内科学
哲学
社会学
几何学
考古
语言学
作者
Xiao-Rui Yan,Song Ci,Tao Pei,Erjia Ge,Le Liu,Wang Xi,Linfeng Jiang
出处
期刊:Cornell University - arXiv
日期:2023-06-25
标识
DOI:10.48550/arxiv.2307.05500
摘要
The swift relaxation of the zero-COVID policy in December 2022 led to an unprecedented surge in Omicron variant infections in China. With the suspension of mandatory testing, tracking this epidemic outbreak was challenging because infections were often underrepresented in survey and testing results, which only involved partial populations. We used large-scale mobile phone data to estimate daily infections in Beijing from November 2022 to January 2023. We demonstrated that an individual's location records of mobile phone could be used to infer his or her infectious status. Then, the derived status of millions of individuals could be summed to reconstruct the citywide spatiotemporal dynamics of infections. We found that the infection incidence peaked on 21 December, and 80.1% of populations had been infected by 14 January 2023 in Beijing. Furthermore, infection dynamics exhibited significant demographic and spatiotemporal disparities. Our work provides a ubiquitous and high-coverage data source for monitoring epidemic outbreaks.
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