Mobility, exposure, and epidemiological timelines of COVID-19 infections in China outside Hubei province

Xiao Fan Liu,Xiao-Ke Xu,Ye Wu

doi:10.1038/s41597-021-00844-8

Xiao Fan Liu, Xiao-Ke Xu + Show 1 more

Open Access

https://doi.org/10.1038/s41597-021-00844-8

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Abstract

The 2019 coronavirus disease (COVID-19) is pseudonymously linked to more than 100 million cases in the world as of January 2021. High-quality data are needed but lacking in the understanding of and fighting against COVID-19. We provide a complete and updating hand-coded line-list dataset containing detailed information of the cases in China and outside the epicenter in Hubei province. The data are extracted from public disclosures by local health authorities, starting from January 19. This dataset contains a very rich set of features for the characterization of COVID-19’s epidemiological properties, including individual cases’ demographic information, travel history, potential virus exposure scenario, contacts with known infections, and timelines of symptom onset, quarantine, infection confirmation, and hospitalization. These cases can be considered the baseline COVID-19 transmissibility under extreme mitigation measures, and therefore, a reference for comparative scientific investigation and public policymaking.

Highlights

Background & SummaryIn the global fight against the COVID-19 pandemic, high-quality data are needed for clinical understanding, mathematical modeling, and policymaking considerations
As of November 20, 2020, 12667 cases are identified with detailed epidemiological information, accounting for 71.1% of a total of 17814 infections in China outside Hubei Province
Five categories of detailed information can be extracted from the above disclosure, i.e., demographic information, past mobility trace, exposure to the virus or potential contact with known infection(s), the timeline of symptom onset, hospitalization, infection confirmation, etc., and clinical symptoms

Summary

Introduction

Background & SummaryIn the global fight against the COVID-19 pandemic, high-quality data are needed for clinical understanding, mathematical modeling, and policymaking considerations. The information contained in the dataset includes the case’s demographic information, e.g., gender, age, and occupation; travel history, e.g., trace and timeline of mobility; exposure to known infections, e.g., when, where, how, and to whom exposed; timelines of case admission, e.g., dates of quarantine, symptom onset, hospital admission, and infection confirmation; and other information, such as clinical symptoms, mentioned in the public disclosure.

Results

Conclusion