论文标题
高频移动性大数据揭示了19.19如何在职业,地点和年龄段的领域分布
A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups
论文作者
论文摘要
随着感染和接种疫苗的增加,一些国家决定不再采取非药物干预措施,并与Covid-19并存。但是,我们对其后果没有全面的了解,尤其是对于大多数人口尚未感染且大多数Omicron传播都保持沉默的中国。本文是首次揭示Covid-19的完整无声传播动态的研究,该动态覆盖了超过70万个实际个人出行轨迹的大数据,而在中国城市中,一周中没有采取任何干预措施,在现有研究中未获得完整性和现实主义的程度。加上Covid-19的经验推断的传播率,我们发现只有70名公民最初被感染,终于有33万人终于被默默地感染。我们还揭示了传输动态的每日周期性周期模式,早晨和下午的山峰。此外,零售,餐饮和酒店工作人员比其他专业更有可能被感染。与所有其他年龄段和职业不同,老年人和退休人员比外面的家中更有可能在家中感染。
As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence , especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home.