Spatiotemporal Characteristics and Factor Analysis of SARS-CoV-2 Infections among Healthcare Workers in Wuhan, China
Peixiao Wang, Hui Ren, Xinyan Zhu, Xiaokang Fu, Hongqiang Liu, Tao, Hu

TL;DR
This study analyzes the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers in Wuhan, revealing patterns, influential factors, and the impact of external measures to inform better protective strategies.
Contribution
It provides a detailed analysis of the spatiotemporal patterns and external factors affecting HCW infections using a new open-source dataset and geographical detector technique.
Findings
Infections followed a log-normal distribution with a peak on January 23, 2020.
Higher infection risks were associated with hospitals closer to infection sources.
Lockdown measures temporarily increased HCW infection probabilities.
Abstract
Studying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure. Existing studies related to HCW infections have emphasized infection rates and protective measures. However, the spatiotemporal patterns and related external environmental factors of HCW infections remain unclear. To fill this gap, an open-source dataset of HCW diagnoses was provided, and the spatiotemporal distributions of SARS-CoV-2 infections among HCWs in Wuhan, China were explored. A geographical detector technique was then used to investigate the impacts of hospital level, type, distance from the infection source, and other external indicators of HCW infections. The results showed that the number of daily HCW infections over time in Wuhan followed a log-normal distribution, with and its mean observed on January 23, 2020 and a standard…
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