Exploring the Spatiotemporal Characteristics of COVID-19 Infections among Healthcare Workers: A Multi-Scale Perspective

Abstract

The outbreak of COVID-19 has constantly exposed health care workers (HCWs) around the world to a high risk of infection. To more accurately discover the infection differences among high-risk occupations and institutions, Hubei Province was taken as an example to explore the spatiotemporal characteristics of HCWs at different scales by employing the chi-square test and fitting distribution. The results indicate (1) the units around the epicenter of the epidemic present lognormal distribution, and the periphery is Poisson distribution. There is a clear dividing line between lognormal and Poisson distribution in terms of the number of HCWs infections. (2) The infection rates of different types of HCWs at multiple geospatial scales are significantly different, caused by the spatial heterogeneity of the number of HCWs. (3) With the increase of HCWs infection rate, the infection difference among various HCWs also gradually increases and the infection difference becomes more evident on a larger scale. The analysis of the multi-scale infection rate and statistical distribution characteristics of HCWs can help government departments rationally allocate the number of HCWs and personal protective equipment to achieve distribution on demand, thereby reducing the mental and physical pressure and infection rate of HCWs.

Risk-based allocation of COVID-19 personal protective equipment under supply shortages

“…(2021) , where the probability of infection depends on the probability of infection per contact with an infected individual at hospital in time period , denoted by , and the number of contacts with infected individuals during period , denoted by . Research on infectious disease modelling suggests using a Poisson distribution when the number of infections is low ( Ren, Wang, Guo, & Zhu, 2021 ). Based on data from the province of Ontario, Canada, the maximum weekly percentage of infected HCWs equals 0.17% ( Government of Ontario, 2021b ), which supports this.…”

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

“…Therefore, in this paper, infection data of healthcare workers is considered as a more accurate method. Compared with patient death data, healthcare worker infections were more easily detected and calculated ( Ren et al, 2021 ; Wang et al, 2020 ). Additionally, we also proposed a novel framework to explore the impact of under-reporting on COVID-19 spatiotemporal distributions using an accurate small sample.…”

“…In the early stage of the epidemic, healthcare worker infections were more easily detected and calculated. Therefore, data on confirmed cases of healthcare workers can more accurately reflect the relevant characteristics of COVID-19 ( Gao et al, 2020 ; Ren et al, 2021 ; Wang et al, 2020 ). Considering this, a novel framework was proposed to evaluate the spatiotemporal characteristics of COVID-19 outbreak based on the infection data of healthcare workers.…”