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Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China from January to March 2020

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Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China from January to March 2020 Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2020.DOI Spatiotemporal Differences of COVID-19 Infection among Healthcare Workers and Patients in China from January to March 2020 PEIXIAO WANG1, XINYAN ZHU1,3,4, WEI GUO1, HUI REN1, TAO HU2 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; (e-mail: [email protected],[email protected],[email protected]) 2Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA 3Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China 4Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan University, Wuhan 430079, China Corresponding author: Xinyan Zhu (e-mail: [email protected]) and Tao Hu (e-mail: [email protected]) This project was supported by the Key Program of National Natural Science Foundation of China (No. 41830645), National Key Research and Development Program of China (Grant Nos. 2018YFB0505500,2018YFB0505503), Funding program: CAE Advisory Project No. 2020-ZD-16. ABSTRACT Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly. INDEX TERMS Healthcare worker infection; patient infection; spatiotemporal distribution; spatiotemporal differences; COVID-19 I. INTRODUCTION To achieve targeted and accurate containment, it is nec- essary to understand the spatiotemporal spread of COVID-19 HE COVID-19 virus was first discovered at the end as time progresses and determine its development trend early- arXiv:2011.09342v2 [physics.soc-ph] 27 Jan 2021 T of 2019, and subsequently spread rapidly worldwide. on [14]. In the early stages of the epidemic, patient infection At present, the world has been heavily hit by the COVID- data were a very important data source. Patient infection 19 pandemic [1], [2]. Since the outbreak of COVID-19, data contains a wealth of information that can be explored many researchers have performed classical, epidemiological, via spatiotemporal analysis [15], [16]. Therefore, many re- mathematical, and statistical analyses to carry out emergency searchers have used this data to explore the spatiotemporal research from the perspectives of pathology [3], [4], epi- characteristics of COVID-19. However, studies using other demiology [5], genomics [6], clinical medicine [7]–[9], and data sources to assess the characteristics of the epidemic molecular biology [10]. However, when a novel infectious in social groups (e.g., healthcare workers) are scarce [17]. disease is prevalent, relevant preventative measures such as Studying the spatiotemporal distributions and differences in self-isolation, restriction of crowd movement and gathering, healthcare worker infections can aid in better formulating and wearing of masks became important emergency contain- epidemic containment policies; however, the spatiotemporal ment measures [11]–[13]. VOLUME 4, 2016 1 PEIXIAO WANG et al.: Preparation of Papers for IEEE ACCESS characteristics of COVID-19 between healthcare workers are areas by combining multi-source data. Loske [26] explored rarely studied. To fill this research gap, the spatiotemporal the relationship between COVID-19 spread and transporta- characteristics and differences between healthcare worker tion volume in food retail logistics by combining transport infections and patient infections were analyzed from the volume data and confirmed patient data. Further, Zhang et perspectives of Wuhan, Hubei (excluding Wuhan), and China al. [27] measured the imported case risk of COVID-19 from (excluding Hubei) by combining the data of healthcare work- inbound international flights by combining both daily dy- ers infection and the data of patients infection. The main namic international air connectivity data and updated global contributions of this article are as follows: COVID-19 data. (1) The spatiotemporal distributions of healthcare worker However, the abovementioned studies are mostly based on infections were analyzed from the perspectives of Wuhan, patient confirmed data. The spatiotemporal distributions of Hubei (excluding Wuhan), and China (excluding Hubei). COVID-19 in other groups are rarely explored. Thus, we an- (2) The spatiotemporal differences among healthcare alyzed the spatiotemporal characteristics of and differences worker infections and patient infections were also analyzed. between healthcare worker infections and patient infections In addition, the study can provide a scientific reference from the perspectives of Wuhan, Hubei (excluding Wuhan), for relevant departments to formulate epidemic containment and China (excluding Hubei). policies from a new perspective. III. STUDY AREA AND DATA SOURCES II. RELATED WORKS Many scientists have researched COVID-19 from different A. STUDY AREA perspectives. In this section, we primarily present a review of As of July 2020, the total number of locally confirmed relevant studies that explored the spatiotemporal distributions COVID-19 cases in China had exceeded 80,000. Of these of COVID-19. cases, 68,135 were confirmed in Hubei Province, among To explore the spatiotemporal distribution of the disease is which 50,340 were confirmed in Wuhan, accounting for mainly to summarize the spread law of COVID-19 to provide 81.5% and 60.80% of the country’s cases, respectively. To the basis for prevention and control. In the early stage of study the temporal and spatial spread of COVID-19 in China, the epidemic, many studies were conducted based on patient the study area was divided into three parts: Wuhan City, confirmed data. For example, Zhang et al. [18] analyzed Hubei Province (excluding Wuhan), and the rest of China. the epidemiological characteristics of COVID-19 in February As shown in Figure 1, Hubei Province is in the central region 2020 based on patient confirmed data and reported the spatial of China, and Wuhan City is in the central region of Hubei distribution of COVID-19 early-on in China. Hu et al. [19] Province. collected global patient confirmed data from different scales and established a data repository to provide data support for B. DATA SOURCES AND DATA PREPROCESSING research related to the spatiotemporal distribution of COVID- 1) Data Sources 19. Wang et al. [20] used spatiotemporal scanning statistics to detect the hotspots of new cases every week, based on The data used in this study were divided into two categories: the confirmed cases of COVID-19 at the county level in Patient Infection Table and healthcare worker’s infection the United States, thereby characterizing the incidence trend. information. The Patient Infection Table was mainly obtained Further, Zhang et al. [21] compared the spatiotemporal char- from the National Health Commission of the People’s Repub- acteristics between COVID-19 and SARS at the provincial lic of China (http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml) level, thereby revealing the spread of COVID-19. Sun et and spanned from January 15, 2020, to June 28, 2020. al. [22] analyzed the regional spatiotemporal changes of the The confirmed information on healthcare workers was ob- epidemic based on the daily new cases in 250 regions of tained mainly from the Chinese Red Cross Foundation South Korea, thereby evaluating South Korea’s containment (https://www.crcf.org.cn/article/20672). The Chinese Red strategy. Cross Foundation distributes relief funds to every healthcare Researchers have also combined patient confirmed data worker with a confirmed case. As of July 31, 2020, 81 batches with other data sources to explore the spatiotemporal spread of confirmed healthcare workers had received foundation of COVID-19. For example, Zhang et al. [23] examined assistance. We first used Python language and web crawler the factors influencing the number of imported cases from technology to obtain 3,741 reports on confirmed healthcare Wuhan and the spread speed and pattern of the pandemic workers from the Chinese Red Cross Foundation and later by combining national flight and high-speed rail data. Based used the Baidu API for address matching. After matching on mobile phone and confirmed patient data, Jia et al. [24] their address, details of the city, province, and county of developed a spatiotemporal “risk source” model to determine each healthcare worker with a confirmed case were obtained. the geographic distribution and growth pattern of COVID-
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