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. 2021 Apr 8;34(3):429–441. doi: 10.1002/leap.1382

Evolution of scientific collaboration on COVID‐19: A bibliometric analysis

Dezhong Duan 1,✉, Qifan Xia 2
PMCID: PMC8250802  PMID: 34230773

Abstract

This paper considers the pattens of international collaboration by analysing publications on COVID‐19 published in the first 6 months of the pandemic. The data set comprised articles on COVID‐19 indexed in the Web of Science Core Collection (WoS CC) downloaded four times between 1 April 2020 and 1 June 2020. The analysis of 5,827 documents revealed that 128 countries, 23,127 authors, and 6,349 institutes published on the pandemic. The data reveal that the three main publishing countries were the USA, China, and England with Italy closely following. Although publication was widely spread, most of the institutions with the highest volume of output were in China. Network analysis showed growth in international cooperation with an average degree of country/region cooperation rising to 23.06 by 1 June. There was also a clear core‐periphery structure to international collaboration. Institutional collaboration was shown to be highly regionalized. The data reveal a high and growing incidence of international collaboration on the pandemic.

Keywords: contributors, COVID‐19, Scientific collaboration

Key points.

  • The US, China, England, and Italy published the most articles on COVID‐19 in the first 6 months, with the US overtaking China by June 2020.

  • International collaboration on articles about COVID‐19 grew rapidly between April and June 2020.

  • Institutional collaborations on COVID‐19 articles tend to be localized indicating close research networks.

  • Network analysis reveals a clear core‐periphery structure of international collaboration on COVID‐19 articles with growing participation of different countries.

INTRODUCTION

Practice has long proved that international cooperation is not only the leading force in the global exploration of cutting‐edge science but also the best way for the world to respond to issues such as resource and environment, climate change, health, and public safety (Adams, 2013; Adams & Loach, 2015; Choi et al., 2015; Freeman, 2010; Narin et al., 1991; Wagner et al., 2019). It took only 6 months from the discovery of the Novel Coronavirus (COVID‐19) to more than 6 million confirmed cases and 300,000 deaths, which not only proves that the COVID‐19 is too contagious to be overcome but also demonstrates the common destiny of all countries and regions in the era of globalization (Nature Editorial, 2020c; Washington, 2020). In fact, when this outbreak was declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by the WHO, it was already indicated that international cooperation is the key to combating this pandemic (Berkley, 2020; Duan et al., 2020; Nature Editorial, 2020a, 2020b; Nature Medicine Editorial, 2020).

International scientific collaboration, an important part of international cooperation, has been given growing attention in innovation economics (Andersen, 2019; Bauder et al., 2018; Cassi et al., 2012, 2015; Gui et al., 2018a, 2018b; Wuestman et al., 2019), S&T policy (Chen et al., 2019; Fung & Wong, 2017; Gazni et al., 2012; Hou et al., 2008; Sun & Cao, 2020), and knowledge production and technology transfer (Aldridge & Audretsch, 2011; Ankrah & Al‐Tabbaa, 2015; Bekkers & Freitas, 2008). Increasingly common and frequent knowledge flows crossing borders not only speed up the process of scientific globalization but also constantly re‐shape the global scientific landscape (Adams, 2013; Adams & Loach, 2015; Royal Society, 2011). International scientific collaboration is the key support of national competitiveness (Bathelt & Henn, 2014; Freeman, 2010). In the era of pandemic, cooperation in virus research is and important win‐win for participating countries/regions. While improving the scientific research capacity, international cooperation also strengthens the capacity in pandemic prevention and control for each country and region (Nature Editorial, 2020c). In the past 5 months, researchers around the world have conducted a large number of in‐depth studies on the structural morphology, gene sequence, pathogenic mechanism, diffusion mode, etc. of the COVID‐19 virus, giving us a gradually clearer understanding of the virus and how to prevent and control the epidemic (Corey et al., 2020; Guan et al., 2020; Tian et al., 2020; Wu et al., 2020; Zhu et al., 2020). Within this are influential achievements jointly completed by researchers from multiple countries and institutions (Drew et al., 2020; Tian et al., 2020).

By exploring scientific collaboration among countries/regions and among institutes on COVID‐19, this paper aims to answer the following two questions: (1) what is the structure of the international scientific collaboration network and the inter‐institution collaboration network on COVID‐19 research? (2) Who are the major contributing countries/regions and institutions participating in the scientific collaboration? The main contributions of this paper are twofold. Firstly, this paper seeks to enrich the literature on scientific collaboration through sorting out the relevant research about COVID‐19. Specifically, it intends to test whether international scientific collaboration on COVID‐19 is consistent with the existing findings on the structure of global scientific cooperation. It also tries to deepen our understanding of international collaboration in virus research.

METHODOLOGY AND DATA

Data

Although widely being criticized for its limitations (Cantner & Rake, 2014; Royal Society, 2011), co‐publication is still one of the best ways to characterize scientific collaboration between authors, between countries/regions or between organizations (Basu & Kumar, 2000; Gui, Liu, & Du, 2019; Gui, Liu, Du, et al., 2019; He, 2009; Lemarchand, 2012; Liu & Gui, 2016; Sun & Cao, 2020; Sun & Grimes, 2016). The publications data analysed here was retrieved from Web of Science Core Collection (WoS CC), by adopting the full counting method (full credit to a country/institutes when at least one of the authors is from this country/institutes) to count the scientific collaborations among countries/regions or among institutes (Gauffriau & Larsen, 2005).

To clearly describe the development of scientific cooperation in the research of COVID‐19, we counted all related publications (articles, reviews, letters and so on) collected on April 1, and collected new publications every half month thereafter. As of June 1, we had collected publications about COVID‐19 at five points in time, which are April 1, April 15, May 1, May 15, and June 1. In addition, due to the difference in the initial naming of the new coronavirus, the publications search was sequentially retrieved through four topic words: novel coronavirus, SARS‐CoV‐2, 2019‐nCoV, and COVID‐19. All publications were published in 2020, and each search was conducted cumulatively, not discretely. The detailed description is as follows. On the Web of Science literature search page, we first selected WoS CC as the search database. Secondly, we selected the advanced search strategy, and use field identifiers and Boolean operators to create the search query, specifically, TS (topic) = novel coronavirus or TS = SARS‐CoV‐2 or TS = 2019‐nCoV or TS=COVID‐19. Thirdly, we selected the literature data published in 2020 in the search results. We repeated the above three‐step search method at five points in time to obtain the accumulated data at each point of time. To understand the changes between every two points in time, by deleting the duplicated part of the data collected at the later point of time, we obtained the newly added data during every time period.

Bibliometric tools

In this article, the bibliometric method is used to analyse the scientific cooperation on COVID‐19. In the process, two kinds of software were used: VOSviewer and ArcGIS. VOSviewer is a software tool for constructing and visualizing bibliometric networks which can be constructed based on citation, bibliographic coupling, co‐citation, or co‐authorship relations (Perianes‐Rodriguez et al., 2016; Van Eck et al., 2010; Van Eck & Waltman, 2010). ESRI's ArcGIS is a geographic information system for processing maps and geographic information. Its ArcMap product can be used to display and analyse the geographic structure of the cooperative network among authors, institutions, cities, and countries (Gui, Liu, & Du, 2019; Gui, Liu, Du, et al., 2019; Liu & Gui, 2016).

By integrating these two kinds of software, we analysed scientific cooperation around COVID‐19 research both at national level and institute level. Specifically, we first used the VOSviewer to analyse the bibliographic data downloaded from WOS CC, drawing the scientific cooperation network among institutes or among countries/regions, obtaining the list of participating institutes or countries/regions, and the cooperation matrix between institutes or between countries/regions. Second, we used GPS Visualizer's Address Locator (www.gpsvisualizer.com/geocoder/) to geocode all participating institutes or countries/regions. Third, we imported the cooperation matrix with geographic information into ArcMap to analyse the geographical structure of scientific cooperation among institutes or among countries/regions.

Network analysis

Network analysis is a powerful tool to reveal the structural characteristics of a scientific cooperation network (Gui, Liu, & Du, 2019; Gui, Liu, Du, et al., 2019). In this article, network analysis was applied to measure the structural characteristics of the scientific cooperation network on COVID‐19. Specifically, the number of nodes and edges indicates the size of the network, that is, the number of countries/regions, institutes, or authors participating in cooperation. Density and average degree measure the cohesion of the network. The average clustering coefficient and the average path length are measures of the small world network (Watts & Strogatz, 1998). In addition, we also applied block modelling in network analysis to study the core‐peripheral structure of the international cooperation network on COVID‐19. The significant core‐peripheral characteristics of the world economic system have been widely proven (Nemeth & Smith, 1985; Smith & White, 1992), and the core‐peripheral structure of the global scientific cooperation network have also been discussed many times (Gui, Liu, & Du, 2019; Gui, Liu, Du, et al., 2019). We used the PAJEK program for block modelling (Waltman et al., 2010), which is a program for network analysis and visualization.

RESULTS

Descriptive analysis

We are interested in the distribution of publications by countries/regions, institutes and authors, and the leading contributing economies and institutes participating in scientific cooperation on COVID‐19. Table 1 shows the descriptive statistics of the main indicators. During the 2‐month observation from April 1 to June 1, the number of articles about COVID‐19 published worldwide grew rapidly, from 808 as of April 1 to 5,827 as of June 1. The number of countries/regions and institutes participating in the research (sourced from author affiliations) also increased from 62 and 851 as of April 1 to 128 and 6,349 as of June 1, respectively. Cooperation is particularly evident in COVID‐19 research. Most of the countries/regions, institutes and authors involved in the research have cooperated with others to some degree.

TABLE 1.

Descriptive statistic of publications and collaborations about COVID‐19.

As of April 1 April 2–15 April 16–May 1 May 2–15 May 16–June 1 As of June 1
In terms of publication
Number of documents 808 457 878 1,493 2,191 5,827
Number of countries/regions 62 68 66 93 103 128
Number of institutes 851 1,044 2,160 2,378 3,241 6,349
Number of Authors 3,029 2,787 4,021 6,433 9,736 23,127
In terms of collaboration
Number of countries/regions participating in scientific collaboration 60 62 57 83 96 122
collaborations among countries/regions 537 642 947 1,614 2,143 5,886
Number of institutes participating in scientific collaboration 801 950 1,760 2,190 2,960 5,879
collaborations among institutes 2,995 4,199 6,420 11,145 15,602 40,384
Number of authors participating in scientific collaboration 2,976 2,547 3,614 6,142 11,245 21,014
collaborations among authors 21,176 27,786 30,561 36,166 81,739 197,428
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Note: The data in the table are de‐duplicated. Institution data are matched by country and institution name, and author data is matched by institution and author name.

The growth of COVID‐19 studies

Despite the increasing number of countries/regions participating in the research, publications on COVID‐19 were highly concentrated in a few countries/regions. China, the US, and England have consistently ranked among the top three in terms of cumulative publications. China was originally leading in terms of publication volume, indicating that China's leading research work laid a solid knowledge base for the world's knowledge of COVID‐19. With the development of the pandemic, the US became prominent as a global scientific centre. As of June 1, the US had surpassed China in the number of publications, reaching 1,389. China ranks second with 1,295 publications, and England ranks third with 616 publications. In addition, Italy, Canada, India, Germany, Australia, and France also have published a large amount of literature on COVID‐19 (Table 2).

TABLE 2.

Number of documents published by main countries/regions at five points in time.

Country/region As of April 1 As of April 15 As of May 1 As of May 15 As of June 1
US 118 248 442 810 1,389
China 246 460 650 934 1,295
England 41 108 196 358 616
Italy 23 68 164 345 599
Canada 29 53 78 150 262
India 12 35 66 165 252
Germany 31 55 86 151 245
Australia 21 45 77 136 242
France 18 29 40 106 202
Iran 6 25 92 125 177
Switzerland 16 38 65 108 151
Spain 6 14 24 59 141
Singapore 12 39 47 83 139
Brazil 8 16 36 68 118
Netherlands 14 26 33 61 102
Japan 20 33 35 58 99
South Korea 27 42 61 75 97
Turkey 0 4 14 55 96
Saudi Arabia 26 32 41 45 68
Chinese Taiwan 11 14 21 43 51
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Similarly, the publication pattern of COVID‐19 at the institute‐level also showed a high uneven degree of concentration (Table 3), that is, most institutes only published one document, and the number of institutes publishing more than 20 documents is only 86 as of June 1. Institutes from China have the highest volume of scholarly output on COVID‐19 research. According to the literature statistics as of April 1, 17 of the top 20 institutes in terms of publications were from China. The CAS, HKU, and HUST ranked among the top three with 27, 21 and 18 publications, respectively. As of June 1, although the number of Chinese institutes in the top 20 decreased to 10, 4 of the top 5 came from China. HUST, WU, and HKU ranked first, second, and third with 143, 102, and 81 documents, respectively. Moreover, institutes from the US, England, Canada, Italy, Iran, Australia also played an important role in COVID‐19 research.

TABLE 3.

Top 20 institutes with the most publications on COVID‐19 at five points in time.

As of April 1 As of April 15 As of May 1 As of May 15 As of June 1
Ins. Articles Ins. Articles Ins. Articles Ins. Articles Ins. Articles
CAS 27 HUST 38 HUST 63 HUST 101 HUST 143
HKU 21 HKU 38 HKU 44 WU 75 WU 102
HUST 18 CAS 35 WU 44 HKU 59 HKU 81
FU 15 FU 33 CAS 37 ZJU 54 ZJU 76
CMU 14 WU 28 ZJU 37 FU 50 HMS 71
ZJU 14 ZJU 25 FU 35 CUHK 47 FU 66
WU 13 CMU 21 CMU 34 CMU 44 UT 65
CUHK 11 SYSU 21 UTMS 32 HMS 44 OU 63
GMU 11 CUHK 20 CAMS 29 OU 44 CUHK 62
SCAU 11 CAMS 19 SYSU 27 UTMS 41 UoM 62
SYSU 11 SJTU 18 SJTU 26 UT 41 CMU 58
UoS 11 SCU 18 OU 26 CAS 40 UCL 58
CAAS 10 LSHTM 17 UCL 25 CAMS 39 UTMS 54
HU 10 GMU 16 CUHK 24 SJTU 39 NUS 53
SJTU 10 PU 16 HMS 24 UCL 36 CAMS 52
SCU 10 TSU 15 PU 22 PU 33 CAS 52
CUMB 9 UoS 15 SCU 22 UoM 32 SJTU 51
HZAU 9 UCL 14 SBUMS 21 SCU 31 UMG 51
U. CAS 9 OU 14 LSHTM 20 SYSU 31 CU 50
CAMS 8 CQMU 13 ICU 19 CU 30 UMB 47
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Abbreviations: CAAS, Chinese Academy of Agricultural Science; CAMS, Chinese Academy of Medical Sciences; CAS, Chinese Academy of Sciences; CMU, Capital Medical University; CQMU, Chongqing Medical University; CU, Columbia University; CUHK, Chinese University of Hong Kong; CUMB, Charité‐University Medicine Berlin; FU, Fudan University; GMU, Guangzhou Medical University; HKU, University of Hong Kong; HMS, Harvard Medical School; HU, Hokkaido University; HUST, Huazhong University of Science and Technology; HZAU, Huazhong Agricultural University; ICU, Imperial College London; LSHTM, London School of Hygiene & Tropical Medicine; NUS, National University of Singapore; OU, Oxford University; PU, Peking University; SBUMS, Shahid Beheshti University of Medical Sciences; SCAU, South China Agricultural University; SCU, Sichuan University; SJTU, Shanghai Jiao Tong University; SYSU, Sun Yat‐Sen University; TSU, Tsinghua University; U. CAS, University of CAS; UCL, University College London; UMB, University of Melbourne; UMG, University of Michigan; UoM, University of Milan; UoS, University of Sydney; UT, University of Toronto; UTMS, Tehran University of Medical Sciences; WU, Wuhan University; ZJU, Zhejiang University.

More and more researchers also participated in COVID‐19 research. The literature statistics as of April 1 showed that 3,029 researchers published studies of COVID‐19 and related fields, and this increased to 23,127 by June 1. In addition, China's noticeable performance at the national and institutional level has not been confirmed at the individual level. In the literature statistics on April 1, only 6 of the top 20 authors were from China (and two authors also received partial support from Chinese institutions), while eight authors were from the HU in Japan. As of April 1, Shi Zhengli, a researcher from CAS published the largest number of articles in the world on COVID‐19 research, reaching 8. As of June 1, 8 of the top 20 authors were from China, with 4 of them from Chinese Hong Kong. As of June 1, Wiwanitkit Viroj, a researcher from DDYPU and HMU had published the largest number of research articles in the world, reaching 26 publications (Table 4).

TABLE 4.

Top 20 authors with the most publications and their related information.

As of April 1 As of June 1
Author Institute Publications Author Institute Publications
Shi, Z. L. CAS 8 Wiwanitkit V. DDYPU and HMU 26
Holmes E. C. FU and UoS 7 Lippi G. VU 17
Drosten C. CUMB 7 Joob B. SMA 15
Akhmetzhanov A. R. HU 7 Memis Z. A. EMU and AU 14
Linton N. M. HU 7 Drosten C. CUMB 12
Nishiura H. HU 7 Nishiura H. HU 12
Memish Z. A. EMU and AU 7 Cowling B. J. HKU 11
Yuen K. Y. HKU 6 Leung G. M. HKU 11
Zhang W. CAS 6 Rodriguez‐Morales A. J. ACI, UTP and FUAA 11
Hayashi K. HU 6 Yang L. HKPU 11
Jung S. M HU 6 Yang Y. ISMMS 11
Kinoshita R. HU 6 Zhang W CAS 11
Kobayashi T. HU 6 He D. H. HKPU 10
Xiao S. HAU 6 Jiang S. B. NYBC and FU 10
Yang Y. HU 6 Li H. CJFH and CAMSPUMC 10
Zumla A. UCL 6 Zumla A. UCL 10
Baric R. S. UNC 5 Akhmetzhanov A R. HU 9
Fang L. HAU 5 Cao B. CJFH, CAMSPUMC, TSU and CMU 9
Feng L. CAAS 5 Li T. S. CAMSPUMC 9
Jiang S. B. NYBC and FU 5 Linton N. M. HU 9
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Abbreviations: ACI, Asociación Colombiana de Infectología; AU, Alfaisal University; CAMSPUMC, Chinese Academy of Medical Sciences & Peking Union Medical College; CJFH, China‐Japan Friendship Hospital; DDYPU, Dr. DY Patil University; EMU, Emory University; FUAA, National Autonomous University of Mexico; HKPU, Hong Kong Polytechnic University; HMU, Hainan Medical University; ISMMS, Icahn School of Medicine at Mount Sinai; NYBC, New York Blood Center; SMA, Sanitation 1 Medical Academy Centre; UTP, Technological University of Pereira; VU, Verona University.

Contributions to scientific cooperation

This section traces network evolution on scientific cooperation around COVID‐19 articles and analyses the countries/regions, and institutions contributing to the promotion of COVID‐19 scientific cooperation.

Cooperation network evolution

According to Table 5, the international cooperation network on COVID‐19 is moving towards intensiveness, with the network density increasing from 0.163 as of April 1 to 0.191 as of June 1. The average degree also increases continuously from 9.633 to 23.06, which means that a country/region has cooperated with 23.06 other countries/regions on average. As of June 1, the density of international cooperation network was only 0.191, indicated that in the first few months of the outbreak, the international cooperation network was relatively sparse. This shows that although the number of countries/regions participating in the COVID‐19 research is increasing, international cooperation is mainly found in a few countries/regions.

TABLE 5.

Topological characteristics of scientific cooperation network on COVID‐19.

Indicators As of April 1 As of April 15 As of May 1 As of May 15 As of June 1
International cooperation network
Nodes 60 77 96 112 122
Edges 289 487 777 1,055 1,407
Density 0.163 0.186 0.170 0.170 0.191
Average degree 9.633 12.649 16.188 18.839 23.06
Average clustering coefficient 0.752 0.749 0.775 0.769 0.766
Average path length 2.095 2.065 2.041 2.028 1.955
Inter‐institute cooperation network
Nodes 801 1,495 2,454 3,980 5,879
Edges 2,725 6,530 12,329 22,572 36,180
Density 0.009 0.006 0.004 0.003 0.002
Average degree 6.804 8.736 10.048 11.343 12.308
Average clustering coefficient 0.857 0.857 0.860 0.851 0.846
Average path length 4.094 3.816 3.849 3.761 3.694
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The density of inter‐institute cooperation networks is generally lower than 0.009 with a continuous downward trend. While the average degree shows an upward trend, increasing from 6.804 as of April 1 to 12.308 as of June 1 (Table 5). Although it is said that the cooperation among countries/regions is undertaken by institutes, when the research scale is placed at the institute level, the global cooperation network on COVID‐19 appears abnormal coefficient and cooperation becomes extremely precious. Besides, based on Watts and Strogatz's work (Watts & Strogatz, 1998) about small‐world network's features, we also found that the scientific cooperation network on COVID‐19 both at national‐level and institute‐level is a typically small‐world network with higher clustering coefficients and shorter average path length compared with a random graph.

Meanwhile, the international cooperation network on COVID‐19 has an obvious core‐periphery structure (Fig. 1), which can be divided into four categories: core, strong semi‐periphery, semi‐periphery, and periphery (Nemeth & Smith, 1985; Smith & White, 1992; Wallerstein, 1974). The international cooperation network on COVID‐19 as of April 1 was a remarkable double‐core pyramid structure, only the US and China located in the core position. As of June 1, China moved down to the strong semi‐periphery group, a single‐core structure of the international cooperation network on COVID‐19 led by the US has been taking shape. In the strong semi‐periphery layer, from April 1 to June 1, except for the change in China, India rose from the semi‐periphery to this level at May 1 but returned at June 1, Saudi Arabia fell to the semi‐periphery at May 1 and remained its status at June 1. However, the number of countries or regions located in the strong semi‐periphery is relatively stable. In the semi‐periphery, the number of countries or regions increased significantly from 9 at April 1 to 40 at June 1. Surprisingly, countries with large numbers of publications were also located in this layer, such as Iran, Switzerland, Spain, Singapore, etc.

FIGURE 1.

FIGURE 1

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The core‐periphery structure of international cooperation network on COVID‐19 at three points in time.

The contributing countries/regions

Using the ArcMap platform, the international scientific cooperation on COVID‐19 at three points in time, as shown in Fig. 2, is visualized geographically. The Changing geography of international cooperation on COVID‐19 confirmed that COVID‐19 research gradually developed from individual countries leading to global participation. The tri‐polar landscape of global science dominated by North America, Asia‐Pacific, and Europe has also been proven in COVID‐19 research. Cooperation between countries generally occurs within or between these three regions, and the US, China, and England are the three key nodes (Tables 6 and 7).

FIGURE 2.

FIGURE 2

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Geographic pattern of international cooperation on COVID‐19 research.

TABLE 6.

International cooperation on COVID‐19 of main countries (regions).

Country/region As of April 1 As of May 1 As of June 1
Partners Collaborations Partners Collaborations Partners Collaborations
US 35 112 70 476 95 1,304
China 31 132 52 353 72 776
England 28 77 60 351 84 972
Italy 21 40 47 245 67 710
India 7 16 41 120 63 289
Germany 30 69 51 215 72 575
Canada 22 57 45 171 68 514
Australia 20 39 36 153 60 472
Iran 2 2 26 52 46 145
Switzerland 19 28 42 121 61 360
France 17 38 37 120 52 374
Singapore 8 13 22 56 44 164
South Korea 13 14 28 62 37 115
Brazil 6 8 38 88 54 223
Netherlands 16 24 34 108 53 309
Spain 18 20 31 108 58 352
Japan 12 17 24 48 55 199
Turkey 0 0 2 2 39 87
Saudi Arabia 24 59 41 115 51 172
Chinese Taiwan 9 13 16 29 34 83
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Note: “Partners” = number of countries (regions) they cooperated with, “Collaborations” = number of international collaborations.

TABLE 7.

Top 20 partnerships (country‐level) with the most frequent cooperation on COVID‐19.

As of April 1 As of May 1 As of June 1
Cooperation pairs Times Cooperation pairs Times Cooperation pairs Times
China and the US 29 China and the US 86 China and the US 189
The US and Saudi Arabia 13 The US and England 45 The US and England 129
China and England 12 China and England 38 The US and Italy 102
China and Canada 11 The US and Italy 36 The US and Canada 89
China and Australia 10 England and Germany 26 China and England 88
The US and Canada 9 China and Australia 23 England and Italy 77
The US and England 9 The US and Canada 23 The US and Australia 70
England and Germany 8 The US and Germany 22 The US and Germany 59
China and Germany 7 England and Italy 21 England and Germany 58
Canada and Saudi Arabia 6 The US and Australia 20 England and Australia 52
Germany and France 6 China and Canada 20 England and Canada 52
The US and France 6 China and India 18 China and Australia 43
China and Saudi Arabia 6 China and Germany 17 Italy and Germany 43
China and Thailand 6 India and Thailand 17 The US and France 40
Germany and Saudi Arabia 5 The US and Saudi Arabia 17 Italy and Spain 40
China and India 5 England and Canada 16 The US and Switzerland 40
India and Thailand 5 China and Thailand 16 China and Canada 38
China and Italy 5 Germany and Italy 15 China and India 38
Canada and Australia 4 The US and Switzerland 15 China and Italy 35
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In the early stage of the outbreak, China played a vital role in promoting international scientific cooperation. Literature statistics as of April 1 showed that China cooperated with 31 countries/regions 132 times. And among the top 20 partnerships, there are 9 pairs with China's participation, 4 of which are in the top 5. Meanwhile, the US and England also performed well in the international scientific cooperation of COVID‐19, conducting 112 and 77 collaborations with 35 and 28 countries/regions respectively. In addition, the US also participated in 5 of the top 20 partnerships. As of May 1, the US cooperated with 70 countries/regions 476 times, surpassing China both in the number of partners and collaborations. While China conducted 353 collaborations with 52 countries/regions and England carried out 351 collaborations with 60 countries/regions. Of the top 20 partnerships, 8 pairs have US's participation, and China and England participated in 7 and 5 pairs respectively. By June 1, as the hub of COVID‐19 global scientific cooperation, the United States was further consolidated. It has cooperated with 95 countries/regions 1,304 times, far more than other countries/regions both in the number of partners and collaborations. Among the top 20 partnerships, there were 8 pairs with US participation, 4 of which are in the top 5. England also surpassed China by conducting 972 collaborations with 84 countries/regions, while China cooperated with 72 countries/regions 776 times. And in the top 20 partnerships, both China and England participated in 6 of them.

Canada in North America, India, Australia, Iran, Singapore, etc. in the Asia‐Pacific, and Italy, Germany, France, Switzerland, etc. in Europe also greatly participate in scientific cooperation on COVID‐19. However, as of now, China and the US are the two most important countries for COVID‐19 research and scientific cooperation. At the five points in time, the closest cooperation relationship always existed between China and the US, increasing from 29 as of April 1 to 189 as of June 1.

Contributing institutes

Chinese institutes also played an important role in promoting cooperation on COVID‐19 among institutes. But over time, the role of institutes in the US (e.g. Harvard Medical School, HMS), Canada (e.g. University of Toronto, UT), England (e.g. University College London, UCL), Germany (e.g. Charité‐University Medicine Berlin, CUMB), and Australia (e.g. University of Sydney, UoS) in scientific cooperation on COVID‐19 also grew rapidly, even more than most institutes in China. Literature statistics as of April 1 showed that CAS and Capital Medical University (CMU) cooperated with 61 and 64 institutes 87 and 83 times, respectively, becoming the double‐core of the inter‐institute cooperation network on COVID‐19. In addition, HUST, CUMB, UoS, and Fudan University (FU), carrying out 59, 58, 56, and 56 collaborations with 40, 38, 47, and 43 institutes, respectively, also played an important role in the scientific cooperation on COVID‐19 (Table 8). Among the top 20 institutional partnerships, Chinese institutes participated in 9 of them. Cooperation between CAS and University of CAS (U. CAS) was the greatest with nine collaborations. By May 1, HKU, CMU, and HUST ranked among the top three with 187, 186, and 178 collaborations, respectively. Regarding the number of partners, HKU, UCL, and HUST ranked among the top three with 155, 126, and 119 partners, respectively. In addition, FU, WU, CAMS, and Oxford University (OU) also played an important role in promoting cooperation on COVID‐19 between institutes. Among the top 20 institutional partnerships, there were 10 pairs with Chinese institute participation. The collaborations between CAS and U. CAS also ranked highest with 15 collaborations.

TABLE 8.

The top 20 institutional cooperation on COVID‐19.

Institution As of April 1 Institution As of June 1
Partners Collaborations Partners Collaborations
CAS 61 87 HUST 235 418
CMU 64 83 HMS 309 409
HUST 40 59 UT 291 398
CUMB 38 58 UCL 254 362
UoS 47 56 UMB 244 343
FU 43 56 CUHK 223 338
CUHK 39 56 CU 244 324
Ins. Pa 50 55 HKU 224 315
UT 49 51 WU 186 306
UCL 33 49 CMU 174 295
PU 41 47 UoS 222 278
ZJU 41 47 CUMB 209 277
AU 34 47 UoM 215 271
CAMS 33 42 OU 189 268
WU 31 42 UW 194 266
GMU 39 41 UP 221 265
HKU 35 39 CAMS 159 260
KAU 30 38 PU 183 258
EMU 32 36 FU 140 236
UW 28 33 UMG 190 235
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Abbreviations: AU, Alfaisal University; Ins. Pa, Institut Pasteur; KAU, King AbdulAziz University; UP, University of Pennsylvania; UW, University of Washington.

As of June 1, HUST and HMS had conducted 418 and 409 institutional collaborations, respectively. There are also 7 institutes that conducted more than 300 institutional collaborations, namely, UT, UCL, University of Melbourne (UMB), CUHK, Columbia University (CU), HKU, and WU. In terms of the number of partners, HMS, the only institute with more than 300 partners, has cooperated with 309 institutes. There are also 11 institutes with more than 200 partners, of which UT and UCL have more than 250 partners. Among the top 20 partnership institutes, there were 6 pairs of Chinese institutes' and 5 pairs from Germany. The collaborations between HUST and WU reached 22, ranking highest among institutional cooperation. An interesting phenomenon is that, contrary to international cooperation, cooperation on COVID‐19 among institutes exhibits significant geographic proximity, that is, inter‐institute cooperation on COVID‐19 mostly occurred within the country or even within the city. Among the top 20 institutional partnerships as of June 1, there was only one transnational partnership (Table 9).

TABLE 9.

Top 20 partnerships (institute‐level) with the most frequent cooperation on COVID‐19.

As of April 1 As of June 1
Cooperation pairs Collaborations Cooperation pairs Collaborations
CAS and UCAS 9 HUST and WU 22
HU and JSTA 7 DDYPU and HMU 20
CUHK and UCL 5 CAS and UCAS 16
CICSPP and HZAU 5 SBUMS and UTMS 16
HUST and WU 5 CUHK and HKU 14
BIH and CUMB 4 BIH and GUMB 13
BIH and FUB 4 BIH and FUB 13
BIH and HBU 4 CMU and CAMS 13
CMU and CAMS 4 CUMB and HBU 13
CMU and HUST 4 FU and SJTU 13
CUMB and FUB 4 NUHS and NUS 13
CUMB and HBU 4 RMH and UMB 13
CAS and CCDCP 4 BIH and HBU 12
DDYPU and HMU 4 CUMB and FUB 12
FUB and HBU 4 FICGOMP and UoM 12
FU and NYBC 4 FUB and HBU 12
HZAU and UGA 4 IUMS and SBUMS 12
AU and CUHK 3 IUMS and UTMS 12
AU and EMU 3 CMU and HUST 11
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Abbreviations: BIH, Berlin Institute of Health; CCDCP, Chinese Center for Disease Control and Prevention; CICSPP, Cooperative Innovation Center for Sustainable Pig Production; FUB, Free University of Berlin; HBU, Humboldt—Universitat zu Berlin; IUMS, Iran University of Medical Sciences; JSTA, Japan Science and Technology Agency; UGA, University of Georgia.

DISCUSSIONS AND CONCLUSIONS

At the time of writing, the COVID‐19 pandemic is still ravaging the world. Tens of thousands of confirmed cases and thousands of deaths are confirmed and announced every day. More extensive and in‐depth cooperation should be carried out on a global scale (Nature Editorial, 2020a, 2020b). This paper attempts to provide a comprehensive picture of scientific collaboration on COVID‐19 research among countries/regions and among institutes within the first few months of the pandemic. The study included 5,827 papers about COVID‐19 published by 6,349 institutions from 128 countries/regions.

We admit that there are some shortcomings in this study. Firstly, we limited our data to the publications retrieved from the Web of Science. Although it is known for its huge amount of data (Cassi et al., 2012; Gui et al., 2018b; Leydesdorff & Wagner, 2008), it is still limited in its inclusion. Secondly, although co‐publications are widely accepted as proxies of scientific collaboration, as mentioned before, scientific cooperation does not necessarily lead to the publication of papers (Cantner & Rake, 2014; Royal Society, 2011). Moreover, cooperation in publishing papers may only be a small aspect of scientific cooperation on COVID‐19. Thirdly, this paper mainly focused on the cooperation, other bibliometric features are not involved, such as citation analysis, hotspot analysis, and community analysis.

Through this bibliometric study, we found some interesting phenomena. First of all, scientific cooperation on COVID‐19 has become more frequent. As of June 1, an increasing number of countries/regions, institutions, and researchers participated in scientific cooperation on COVID‐19. The international scientific community generally recognizes that collaboration is the right way to work to overcome the epidemic and build a community of human health. Secondly, we discovered that the tri‐polar pattern of international scientific cooperation controlled by North America, Asia‐Pacific, and Europe (Gui, Liu, & Du, 2019; Gui, Liu, Du, et al., 2019) is clearly portrayed in COVID‐19 research. In these three regions, the US, China, England, Canada, Germany, India, and Australia are the core hubs of the international cooperation network for COVID‐19 research. Particularly, the US is playing an increasingly important role in research and international cooperation on COVID‐19, reflecting its status as a global scientific centre. Most countries/regions regard the US as the strongest scientific partner. Thirdly, China has played a vital role in the scientific research and cooperation on COVID‐19, which is not only reflected in the number of published papers (Duan et al., 2020) but also in its extensive international cooperation (Mo & Zhou, 2020; Wu et al., 2020; Zhou et al., 2020). Fourth, China and the US were the closest partners in the current international scientific cooperation of COVID‐19. Regardless of the current tense international relations between China and the US, in the face of the epidemic the institutions and researchers of the two countries still carried out close scientific cooperation.

Biographies

D. Duan

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Q. Xia

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