| Literature DB >> 32914141 |
Yue Gong1, Ting-Can Ma2,3, Yang-Yang Xu4, Rui Yang5,6, Lan-Jun Gao7, Si-Hua Wu8, Jing Li6,9, Ming-Liang Yue2, Hui-Gang Liang2, Xiao He1, Tao Yun10.
Abstract
In December 2019, an outbreak of pneumonia, which was named COVID-2019, emerged as a global health crisis. Scientists worldwide are engaged in attempts to elucidate the transmission and pathogenic mechanisms of the causative coronavirus. COVID-19 was declared a pandemic by the World Health Organization in March 2020, making it critical to track and review the state of research on COVID-19 to provide guidance for further investigations. Here, bibliometric and knowledge mapping analyses of studies on COVID-19 were performed, including more than 1,500 papers on COVID-19 available in the PubMed and China National Knowledge Infrastructure databases from January 1, 2020 to March 8, 2020. In this review, we found that because of the rapid response of researchers worldwide, the number of COVID-19-related publications showed a high growth trend in the first 10 days of February; among these, the largest number of studies originated in China, the country most affected by pandemic in its early stages. Our findings revealed that the epidemic situation and data accessibility of different research teams have caused obvious difference in emphases of the publications. Besides, there was an unprecedented level of close cooperation and information sharing within the global scientific community relative to previous coronavirus research. We combed and drew the knowledge map of the SARS-CoV-2 literature, explored early status of research on etiology, pathology, epidemiology, treatment, prevention, and control, and discussed knowledge gaps that remain to be urgently addressed. Future perspectives on treatment, prevention, and control are also presented to provide fundamental references for current and future coronavirus research.Entities:
Keywords: COVID-19; SARS-CoV-2; bibliometric analysis; knowledge map; knowledge scape; research status
Year: 2020 PMID: 32914141 PMCID: PMC7403001 DOI: 10.1016/j.xinn.2020.100027
Source DB: PubMed Journal: Innovation (Camb) ISSN: 2666-6758
Figure 1Temporal Distribution of COVID-19-Related Early Publications in 2020
Figure 2Networks Formed by Collaborative Countries on COVID-19 Research.
The size of the nodes represent the number of papers. The lines between two nodes represent collaboration links, the intensity of which is proportional to the thickness of the line.
Number of Publications Resulting from Collaborations between China and Collaborative Countries, by Research Subdomain
| Etiology | Diagnosis | Epidemiology | Treatment | Prognosis | Nursing | Prevention and Control |
|---|---|---|---|---|---|---|
| US (9) | US (4) | US (11) | Japan (3) | Canada (1) | Canada (2) | US (8) |
| Japan (2) | UK (1) | Saudi Arabia (2) | US (1) | Australia (1) | Australia (2) | |
| Australia (1) | Germany (1) | Japan (2) | Italy (1) | Canada (2) | ||
| Austria (1) | Japan (1) | Italy (2) | Bangladesh (1) | |||
| Germany (1) | Switzerland (1) | Germany (2) | Sweden (1) | |||
| Saudi Arabia (1) | the Netherlands (1) | Canada (2) | Denmark (1) | |||
| Norway (1) | Thailand (1) | Iran (1) | ||||
| Sweden (1) | Malaysia (1) | |||||
| Malaysia (1) | Norway (1) | |||||
| India (1) | UK (1) | |||||
| Egypt (1) | ||||||
| Belgium (1) |
Figure 3Distribution of Chinese Articles on COVID-19 across Major Cities and Provinces, by Research Subdomain (Number of Publications ≥50).
Publications on COVID-19 Resulting from Research Collaborations by Provinces and Cities in China
| Province/City | No. Chinese Papers | No. Inter-provincial Collaborative Papers | Ratio of Inter-provincial Collaborative Papers (%) |
|---|---|---|---|
| Beijing | 256 | 119 | 46.5 |
| Hubei | 177 | 53 | 29.9 |
| Jiangsu | 73 | 38 | 52.1 |
| Shanghai | 100 | 35 | 35.0 |
| Guangdong | 105 | 33 | 31.4 |
| Tianjin | 47 | 24 | 51.1 |
| Sichuan | 100 | 22 | 22.0 |
| Liaoning | 30 | 22 | 73.3 |
| Hunan | 55 | 18 | 32.7 |
| Shaanxi | 51 | 18 | 35.3 |
| Chongqing | 61 | 16 | 26.2 |
| Zhejiang | 51 | 13 | 25.5 |
| Shandong | 32 | 13 | 40.6 |
| Henan | 39 | 12 | 30.8 |
| Total | 1,002 | 184 | 18.4 |
Figure 4SARS-CoV-2 Knowledge Scape.
Different colors represent different research subdomains. The gray hollow circle represents the intersection of key words. Different research subdomains will involve the overlap of key words, such as the "nuclear acid detection" in the topic "testing and sampling methods" and the "morphology, structure, replication" in "pathway." Therefore, the two research subdomains are anchored together at this node, represented by a hollow circle, with two lines pasted together.