Abstract
BACKGROUND: Studying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure. AIM: To describe the spatiotemporal distributions of SARS-CoV-2 infections among HCWs in Wuhan, China. METHODS: In this study, an open-source dataset of HCW diagnoses was provided. 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. FINDINGS: The number of daily HCW infections over time in Wuhan followed a log-normal distribution, with its mean observed on January 23(rd), 2020, and a standard deviation of 10.8 days. The implementation of high-impact measures, such as the lockdown of the city, may have increased the probability of HCW infections in the short term, especially for those in the outer ring of Wuhan. The infection of HCWs in Wuhan exhibited clear spatial heterogeneity. The number of HCW infections was higher in the central city and lower in the outer city. CONCLUSION: HCW infections displayed significant spatial autocorrelation and dependence. Factor analysis revealed that hospital level and type had an even greater impact on HCW infections; third-class and general hospitals closer to infection sources were correlated with especially high risks of infection.