A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

• Main Authors: Hou, X. (Author), Yeung, C.H (Author), Zeng, A. (Author), Zhang, J. (Author), Zhao, C. (Author)
• Format: Article
• Language: English
• Published: NLM (Medline) 2023
• Subjects: aged; Aged; big data; Big Data; China; coronavirus disease 2019; COVID-19; human; Humans; occupation; Occupations; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 15537358 (ISSN)
• DOI: 10.1371/journal.pcbi.1011083

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. Copyright: © 2023 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.