College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, P.R. China.
Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security, Shijiazhuang, P.R. China.
PLoS Comput Biol. 2023 Apr 27;19(4):e1011083. doi: 10.1371/journal.pcbi.1011083. eCollection 2023 Apr.
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 aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations 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, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home.
随着感染和接种人群的增加，一些国家决定不再实施非药物干预措施，与 COVID-19 共存。然而，我们对其后果还没有全面的了解，特别是在中国，大多数人还没有感染，大多数 Omicron 传播是无声的。本文旨在通过基于代理的模拟揭示 COVID-19 的完整无声传播动态，该模拟覆盖了一个中国城市一周内超过 70 万人的真实个体移动轨迹大数据，而且没有采取任何干预措施，其完整性和现实性在现有研究中是没有达到的。结合从经验推断出的 COVID-19 的传播率，我们发现一个惊人的事实，即最初只有 70 名公民被感染，最终却有 33 万人无声无息地被感染。我们还揭示了传播动态的一个特征性的每日周期模式，其峰值出现在早上和下午。此外，通过推断个人职业、访问地点和年龄组，我们发现零售、餐饮和酒店工作人员比其他职业更容易感染，老年人和退休人员在家里比在外面更容易感染。
