  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 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 HCWs in the outer ring of Wuhan. The infection of HCWs Wuhan
exhibited clear spatial heterogeneity. The number of HCW infections was higher
in the central city and lower in the outer city. Moreover, HCW infections
displayed significant spatial autocorrelation and dependence. Factors analyses
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. These findings can aid
national epidemic prevention and control departments to understand the
spatiotemporal distributions of viral transmission resulting in HCW infections,
as well as external influencing factors, which can facilitate the protection of
HCWs in China.

Physics and Society