Dr. LIU Xiaofan, teamed with Prof. XU Xiao-Ke (Dalian Minzu University) and Prof. WU Ye (Beijing Normal University), released a massive dataset containing detailed epidemiological information of more than 14,000 COVID-19 cases on Scientific Data today.
The data curation team, composed of more than 20 research assistants from Beijing Normal University and Dalian Minzu University, has been monitoring the online daily COVID-19 reports from Chinese health committees (except Hubei province) since early 2020. The dataset contains the movement trajectories, suspected contact scenarios, and epidemiological history of more than 14,000 cases. Over the past year, the dataset has proven to be a valuable asset for pandemic research and supported publications in many top journals, including Science, Clinical Infectious Disease, and Emerging Infectious Disease.
This open-access dataset is stored synchronously on GitHub and figshare data repositories and is continuously updated on a bi-weekly basis. Here, we would like to call on researchers and interested parties worldwide to visit our GitHub website, join the data processing team, and help us check, correct, update, and maintain the dataset. We believe that this dataset would for sure lay a solid ground for future scientific research.
The Chinese version of this dataset has been previously released by The Paper press (澎湃). For details, please click this link.
Moreover, the research team studied the mechanism, development, and control of the pandemic from multiple perspectives such as data mining, mathematical modeling, and computer simulation. The results have been published in journals such as Clinical Infectious Disease and Global Media Studies (see the list below for papers that are already published or released online).
X.F. Liu, X.-K. Xu*, and Y. Wu*, “Mobility, exposure, and epidemiological timelines of COVID-19 infections in China outside Hubei Province,” Sci Data 8, 54 (2021).
L. Zhang, J. Zhu, X. Wang, J. Yang, X.F. Liu*, and X.-K. Xu*, “Characterizing COVID-19 transmission: incubation period, reproduction rate, and multiple-generation spreading,” Front. Phys. 8: 589963 (2021).
Xu, X-K.+, Liu, X-F.+, Wu, Y.+, Ali, S. T.+, Du, Z.+, Bosetti, P., Lau, E. H. Y., Cowling, B. J., & Wang, L.*. “Reconstruction of Transmission Pairs for novel Coronavirus Disease 2019 (COVID-19) in China: Estimation of Super-spreading Events, Serial Interval, and Hazard of Infection,” Clinical Infectious Diseases, ciaa790 (2020).
H.-C. Sun, X.F. Liu, X.-K. Xu*, and Y. Wu*, “Analysis of COVID-19 Spreading and Prevention Strategy in Schools Based on Continuous Infection Model,” Acta Physica Sinica (物理学报), 69(24): 240201 (2020).
W.-J. Cao, X.-F. Liu, Z. Han, X. Feng*, L. Zhang*, X.F. Liu, X.-K. Xu, and Y. Wu, “Statistical analysis and autoregressive modeling of confirmed coronavirus disease 2019 epidemic cases,” Acta Physica Sinica (物理学报) 69(9): 090203 (2020).
刘肖凡*, 吴晔, & 许小可. “媒体在流行病暴发事件中的干预作用: 基于传染病模型理论和新型冠状病毒疫情案例的分析,” 全球传媒学刊, 7(1), 169-185 (2020).
+: equally contributed; *: corresponding