Taking “institution” as the node type, the map produced 316 nodes and 181 links. In total, 1,711 articles came from 316 institutions. The top five institutions that produced the most articles were: University of Chinese Academy of Sciences; School of Architecture, Tsinghua University; Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences; China Academy of Information and Communications Technology; School of Information Management, Wuhan University. There were some collaborations between these institutions. However, the centrality of the collaborations was 0, indicating that the collaboration among research institutions was insignificant.
Taking “author” as the node type, the map produced 212 nodes and 386 links. The 1,711 articles were written by 212 authors. Removing the influence of the same name, the top five authors are Tao Pei, Ci Song, Xiaoxiang Zhang, Ying Long, and Chenghu Zhou.
These authors are regarded as active researchers in the area. References with high citation frequency by active authors can be regarded as hot topics in the research field. The most highly cited article among his publications on COVID-19 prevention and control is “Multi-level Spatial Distribution Estimation Model of the Inter-Regional Migrant Population Using Multi-Source Spatio-Temporal Big Data: A Case Study of Migrants from Wuhan During the Spread of COVID-19,” published in the Journal of Geo-Information Science, focusing on the estimation of the spatial distribution of the displaced population in Wuhan based on big data (4). This paper showed that big data can quickly respond to public health emergencies and provide scientific numeric support for decision-making. There were some collaborations among authors, but the centrality of the collaborations was 0, indicating insignificant collaboration among authors.
Taking “term” and “keyword” as the network nodes, after combining synonyms and deleting of meaningless keywords, the generated keyword knowledge map contains 179 nodes, 299 links, and a network density of 0.0188 (Figure 1). The top 20 keywords and their frequency centrality are shown in Table 1.Figure 1.
Co-occurrence of 179 keywords related to the application of new information technologies to the COVID-19. Each dot in the graph represents a keyword, and the lines indicate the relationship between the keywords. It was evident that the new information technologies in the prevention and control of the COVID-19 epidemic were large and interconnected.
Number Frequency Centrality Key words 1 613 0.76 Coronavirus Disease 2019 (COVID-19) 2 239 0.43 Novel coronavirus pneumonia 3 148 0.35 Epidemic prevention and control 4 119 0.31 Big data 5 107 0.46 Artificial intelligence 6 54 0.01 Internet+ 7 49 0.27 Online public opinion 8 45 0.06 Online teaching 9 38 0.12 Telemedical 10 26 0.08 Blockchain 11 18 0.02 Internet of things 12 18 0.04 5G 13 17 0.06 Emergency management 14 17 0.02 Outbreak prediction 15 14 0.02 New media 16 13 0.10 Social governance 17 12 0.43 Coping strategies 18 12 0 Informatization 19 12 0.29 Pneumonia 20 12 0.06 Epidemic information visualization
Table 1. The top 20 keywords about the application of new information technology to prevention and control of COVID-19, December 24, 2020.
The frequency of keywords showed that the application of new IT in the prevention and control of COVID-19 mainly focused on big data, artificial intelligence, internet, blockchain, IOT, and 5G. It was mainly used in public opinion monitoring, online teaching, telemedical system, emergency management, outbreak prediction, and epidemic information visualization. The details of six hot topics about new IT were as follow:
1) Big data
As shown in Figure 1 and Table 1, the most often used new IT was big data. Big data were applied in the analysis of online public opinion, population movement, close contacts investigation, timely acquisition of epidemic information, and the estimation of the spread of information (5).
AI combined with computed tomography (CT) were used to assist doctors in diagnostics. AI was also combined with big data to improve the epidemic prediction model, as well as being combined with robotics to perform ward management (6).
3) Internet Plus (Internet+)
Thanks to Internet+, telemedical and online teaching were also in the top 20 co-occurring keywords. In order to reduce the flow of people and decrease in-person transits, educational institutions adopted online teaching through the slogan of “No In-Person Teaching, No Suspension of Studying”. Most colleges and universities relied on the internet to carry out online teaching in the form of live or recorded broadcasting (7). In the medical field, many public hospitals opened internet diagnosis and treatment services, such as fever clinics and COVID-19 symptoms consultations, to re-allocate medical resources, optimizing the usage of medical resources (8).
Blockchain was mainly used for emergency material supply management in COVID-19 prevention and control. Blockchain could provide anti-counterfeiting and traceable services for rescue materials. The materials embedded with blockchain technology assisted in keeping accurate transportation records, which effectively promoted information exchange and resource coordination during the process of material allocation. Blockchain could also be coordinated with technologies like artificial intelligence, health codes, IOT, and private chains to investigate close contacts (9).
The IOT was combined with artificial intelligence and blockchain for auxiliary diagnosis and treatment, the delivery of rescue materials, and management of community residents (10).
In the COVID-19 epidemic, 5G provided network infrastructure for square-cabin (Fangcang Shelter) hospitals, Huoshenshan Hospital, and Leishenshan Hospital. It also provided network support for the hospitals' telemedicine and robots, and other information facilities (11).
Analysis of the Institutions
Analysis of the Authors
Analysis of Keyword Cooccurrence
|①||NCP: novel coronavirus pneumonia.|