Network relationships of U.S. government health agencies in relation to the Covid-19 pandemic

Seungil Yum

Introduction. Government institutes, such as the Centers for Disease Control and Prevention and the US Department of Health and Human Services, play important but different roles dealing with COVID-19 in the USA.
Method. This study used NodeXL between June 7 and June 8 to visualise the social networks of the Centers and the Department by collecting 5,166 (the Centers) and 2,940 tweets (the Department).
Analysis. Examined the characteristics of centrality of the two networks and used cluster analysis to identify communication groups.
Results. Findings confirmed that the Centers demonstrate characteristics of people-based online networks, whereas the Department appears as an institutes-based online networks. Second, offline social networks play an essential role in online social networks. Third, the Centers had a greater impact on the networks in the Department network than the effect the Department had on the Centers. Fourth, some key players, such as the World Health Organization, play a pivotal role in both networks.
Conclusions. Governments should explore the different types of health information of government institutes to effectively cope with the COVID-19 pandemic.

DOI: https://doi.org/10.47989/irpaper883

Introduction

Since 2019, coronavirus (SARS-CoV-2, COVID-19) has spread to all over the world, making this a priority for government policy and pandemic control (Bruns et al., 2020; Gruzd and Mai, 2020; Jiang et al., 2020; Mizumoto et al., 2020; Ndinojuo, 2020; Sanders et al., 2020; Singhal, 2020; Sohrabi et al., 2020; Yum, 2020). There were different approaches by governments (Arentz et al., 2020; Clerkin et al., 2020; Fang et al., 2020; Salehi et al., 2020; Wu and McGoogan, 2020). Many countries increased the scale of response in March 2020 (Hale et al., 2020).

This study explores the information role of the Centers for Disease Control and Prevention (hereafter, the Centers) and the Department of Health and Human Services (hereafter, the Department of Health) in the USA, using social network analysis based on Twitter, which was one of the most popular online network services in early 2020 (Khan et al., 2021).

Literature review

COVID-19 is an infectious disease, and the most common symptoms of COVID-19 are fever, dry cough, anosmia, and tiredness, but symptoms vary in severity, and asymptomatic people with COVID-19 can infect their close contacts (Yu and Yang, 2020). Comprehensive studies on transmission from asymptomatic patients are difficult to conduct, as they require testing of large population cohorts and more data are needed to better understand and quantify the transmissibility of COVID-19 (World Health Organization, 2020a).

The World Health Organization plays a leading role in guidelines and responses for COVID-19 and published a comprehensive package of guidance documents for countries, covering topics related to the management of COVID-19 on 10 January 2020. The Organization declared the novel coronavirus outbreak a public health emergency of international concern with the highest level of alarm on 30 January 2020. The Organization issued a consolidated package of existing guidance covering the preparedness, readiness and response actions for four different transmission scenarios: no cases, sporadic cases, clusters of cases and community transmission on March 7 (World Health Organization, 2020b). Table 1 outlines the roles of national and international organizations in response to the pandemic.

Table 1. Summary of health institutes and organizations
Dimension	Region	Name	COVID-19 relevant information facet
International	World	World Health Organization	The leading organization involved in the global coordination for mitigating the COVID-19 pandemic
	Europe	European Centre for Disease Prevention and Control	Provides the weekly surveillance report on COVID-19 in the EU/EEA and the UK using the available data compiled from multiple sources.
	Africa	Africa Centres for Disease Control and Prevention	Strengthens the capacity and capability of Africa's public health institutions as well as partnerships to detect and respond quickly and effectively to COVID-19.
National	Canada	Public Health Agency of Canada	Releases total cases, active cases, recovered cases, number of people tested or deaths in Canada over time.
	The UK	Public Health England	Publishes national COVID-19 surveillance reports, including weekly summary of findings monitored through various COVID-19 surveillance systems.
	The USA	Centers for Disease Control and Prevention	Conduct critical science and provides health information that protects the USA against COVID-19. For example, the Centers provide the COVID Data Tracker for visualizing cases, deaths, and trends of COVID-19 in the U.S.
	The USA	United States Department of Health and Human Services	Works with state, local, tribal and territorial governments, public health officials, health care providers, researchers, private sector organizations and the public to execute a whole-of-America response to the COVID-19 pandemic to protect the health and safety of the American people.

In the USA, the Centers and the Department of Health play an essential role in controlling the COVID-19 pandemic and are also connected with each other to cope with the COVID-19 pandemic. The Centers are a scientific health protection agency to protect people from health, safety and security threats. Its main goal is to protect public health and safety through the control and prevention of disease, injury, and disability in the USA and internationally. It especially focuses its attention on infectious disease, food-borne pathogens, environmental health, occupational safety and health, health promotion, injury prevention and educational activities designed to improve the health of United States citizens. The Centers also conduct research and provide information on the COVID Data Tracker for visualising cases, deaths, and trends of COVID-19 in the USA.

The Department of Health is a cabinet-level executive branch department of the US federal government with the goal of protecting the health of people and providing essential human services. Its mission is improving the health, safety, and well-being of people. The Department of Health works with state, local, tribal and territorial governments, public health officials, health care providers, researchers, private sector organizations and the public to execute a whole-of-America response to the COVID-19 pandemic. Both organizations work with many other organizations but the nature of their social networks for this pandemic response has not been investigated.

Research method

This study employed social network analysis to examine the informational role of the Centers andthe Department of Health for COVID-19. This is an interdisciplinary method developed mainly by sociologists to show the importance of relationships, such as multiplexity, centrality, mutuality, or reciprocity, among interacting units, such as individuals, groups, organizations, or societies (Cantner and Graf, 2006; see also Aggarwal, 2011; Bruggeman and Bruggeman, 2008; Butts, 2008; Durland and Fredericks, 2005; Scott, 1988; Van der Hulst, 2009; Zaphiris and Pfeil, 2007 for the basics of social network analysis). Social network analysis has been applied in information research (du Preez and Meyer, 2016; Johnson, 2019; McBirnie and Urquhart, 2011 (who employ network analysis to highlight the form of interaction patterns in serendipity); McKenzie, 2005; Mowbray et al., 2017; Munoz-Munoz and Miron-Valdivieso, 2017; Park and Park, 2016).

This study used Twitter data to demonstrate social networks of people for COVID-19 across public key players, such as institutes, politicians, media and organizations. Twitter has been widely employed for library and information science research (Hicks et al., 2020; Kjellberg et al., 2016; Stvilia and Gibradze, 2014; Sullivan, 2019; Torres-Salinas et al., 2011). Ewbank (2015) employs a social media analysis of #savelibraries and #getESEAright to explore library advocacy through Twitter.

This study employed NodeXL to examine social networks of the Centers and the Department of Health for COVID-19. NodeXL is a visualisation software program, which supports social networks and content analysis. NodeXL allows scholars to collect, analyse, and visualise a variety of networks containing the elements of a graph structure, such as edges and nodes (Bonsignore et al., 2009). NodeXL provides various visualisation tools and graph drawing layout algorithms, such as Fruchterman-Reingold and Harel-Koren. NodeXL can be used for collecting data from a variety of social media platforms, such as Twitter, YouTube, Flickr, and MediaWiki. NodeXL has been extensively employed as a social network method in various academic fields (Kim et al., 2013).

This study identified the top twenty Twitter accounts for the period between June 3 and June 10, 2020 based on the keyword COVID-19 and the Twitter accounts of the Centers and the Department of Health. NodeXL allows scholars to access Twitter for COVID-19 since 2019 regardless of the research period. The selected data set for the analyses (June 7 and June 8) was based on the number of Twitter users, communications, and suitable content. This is because there would be some differences for Twitter data between June 3 and June 10, 2020 since some users upload, delete, retweet, mention, or reply to COVID-19 during the period. For example, this study chose 10,010 users rather than 10,000 users (when other conditions are the same) for the best result, although it is a trivial change. In-degree centrality was used as an indicator of the main near neighbors to the Centers or the Department of Health (see Appendix Table 1 for descriptive statistics).

In-degree centrality, out-degree centrality, and betweenness centrality of the the Centers and the Department of Health networks were identified. In graph theory and network analysis, centrality refers to the positions of individual vertices within the network. Degree centrality is defined as the number of links incident upon a node (i.e., the number of ties that a node has). If the network is directed (i.e. ties have direction), then there are two separate measures of degree centrality, in-degree and out-degree (Hansen et al., 2020) (Figure 1).

Betweenness centrality shows how many times a vertex is located on the shortest path between two other vertices. The higher a user’s betweenness centrality, the more important they are for transmitting information through a network (Hansen et al., 2020) (see Figure 1). The three methods are calculated as follows:

(A)     In-degree centrality: Cⁱ_i=Σ_{j;j ≠i}y_j,i
(B)    Out-degree centrality: C⁰_i=Σ_{j;j ≠i}y_j,i
(C)    Betweenness centrality: C^b_i=Σ_j<kb_j,k (i)/b_j,k

Where C_i is the centrality of node i, b_j,k = the number of the shortest links that pass through the node between j and k, and b_j,k (i) = the number of the shortest links that pass through the node between j and k going through i. Directed graphs use the convention of connecting nodes with arrows that have arrow heads, showing who is directing the tie towards whom (Hanneman and Riddle, 2005).

p883fig1 — Figure 1. Centrality (X > Y)
A: In-degree, B: Out-degree, C: Betweenness.

Next, this study employed cluster analysis by using the Clauset-Newman-Moore cluster algorithm (see Figures 5 and 6). Cluster analysis is a multivariate statistical methodology that explores data sets and identifies them into relatively homogeneous clusters that between-group variation is maximised and within-group variation is minimized. Each group should have objects that have common characteristics in the group and are as different from those in other groups as possible (Aldenderfer and Blashfield 1984; Everitt et al. 2001; Pálsdóttir, 2008). It is the main task in exploratory data mining and a common technique for statistical data analysis in many fields including social network analysis. Cluster analysis is also widely utilised for information research (Kim, 2010; Nelissen et al., 2017; Ortega Egea et al., 2007; Pálsdóttir, 2008; Sundling, 2017). For example, Ortega Egea et al. (2007) employ cluster analysis to distinguish the different groups of European Internet users. Kim (2010) uses cluster analyses to explore the develop a framework of information requirements for scientists based on 137 respondents. The Clauset-Newman-Moore cluster algorithm is one of the best methods for big data analysis (Vieira et al., 2014). This is because other traditional algorithms, such as the Kernighan–Lin algorithm, spectral partitioning, or hierarchical clustering, work well for some specific cases, but perform poorly in more general types. In contrast, the Clauset-Newman-Moore cluster algorithm is considerably faster than most well-known algorithms and allows scholars to explore huge social networks (Clauset et al., 2004). The equation is as follows:

Where Q is the modularity, 1/2m ∑_vw[A_vw is the number of edges in the graph, v and w are nodes, K_v is the degree of a node v, δ function δ(i, j) is 1 if i = j and 0 otherwise, and C is the community.

Results

In Figures 2-6, graphs represent networks of communication for COVID-19 between the Centers and the Department of Health according to in-degree centrality, out-degree centrality, and betweenness centrality, and the Clauset-Newman-Moore cluster algorithm. The graphs are made up of vertices and edges. Figures 2-4 show that the social networks of the Centers and the Department of Health are differentiated by centrality types. In this study, a circle represents the vertex that is each Twitter user. The labels of the top twenty Twitter accounts are identified in Table 3 for the Centers and Table 4 for the Department of Health. A line represents the edge that is a communication network extending from one Twitter user to another Twitter user. Each communication has two or more tweets to which it is attached, called its endpoints. Twitter users in the inner circle tend to play a more important role in social networks more than those in the outside circle. In-degree centrality in Figure 2 describes the number of communication networks that others have initiated with a Twitter user. For instance, if the Centers were mentioned 100 times by other Twitter users, its in-degree centrality would be 100. Out-degree centrality in Figure 3 visualises the number of communication networks that a Twitter user has initiated with others. If the Department of Health mentioned 100 other users in its tweet, its out-degree centrality would be 100. Betweenness centrality in Figure 4 shows how many times a Twitter user is placed on the shortest path between other Twitter users. For example, the more people depend on the Centers to make social networks with other Twitter users, the higher the value of the Centers' betweenness centrality.

p883fig2b — Figure 2: Social networks based on in-degree centrality (top: the Centers and bottom: the Department of Health)

p883fig3b — Figure 3: Social networks based on out-degree centrality (top: the Centers and bottom: the Department of Health)

p883fig4b — Figure 4. Social networks based on betweenness centrality (top: the Centers and bottom: the Department of Health)

Figure 5 highlights the social networks of the Centers and the Department of Health based on the Clauset-Newman-Moore cluster algorithm by laying out the graph in the entire graph pane. The algorithm tries to place collections of densely connected Twitter users into separate groups. The same color Twitter users represent that they belong to the same group created by the Clauset-Newman-Moore cluster algorithm. Each Twitter user is assigned to exactly one group with the same color. The number of Twitter user and communications in each group is highly differentiated by the characteristics of groups. In the Centers networks, the Centers are in the central part of the whole networks, and most key players are concentrated in the inner circle. Some key players, such as Donald Trump (DT), The New York Times (NYT), and US FDA (FDA), are placed in the right side of the inner circle. In the Department of Health networks, while the Department of Health is placed in the central part of the inner circle, the key players show more dispersed patterns. For instance, six key players are placed between the inner circle and the outer circle, and they are all around the inner circle.

p883fig5b — Figure 5: Social networks for the typical case (top: the Centers and bottom: the Department of Health)
Note. Dot colour identified by same-colour label on diagram.

The Centers and the Department of Health also show different social networks for COVID-19 according to groups (see Figure 6). Figure 6 places the top twenty key players and each sub-group in the boxes to improve the clarity of the network visualization. In the Centers networks, group 1 shows dominant communication networks with other groups. Half of the key players (ten out of twenty), such as the World Health Organization, Donald Trump, and the White House, are in group 2 (see Table 4 and 5 for the groups). In contrast, key players are relatively distributed to many groups in the Department of Helath network (e.g. four key players are in group 1, and five key players are concentrated in group 2). Table 3 demonstrates that the Centers and the Department of Health show different characteristics of vertices and unique edges within the top ten groups.

Table 3. Vertices and unique edges in 10 groups
Group	Centers for Disease Control				Department of Health
Group	Vertices	Unique edges	Proportion of vertices	Proportion of unique edges	Vertices	Unique edges	Proportion of vertices	Proportion of unique edges
G1	2985	4031	57.8	40.7	616	1198	21.0	12.2
G2	879	2185	17.0	22.0	556	1071	18.9	10.9
G3	191	439	3.7	4.4	542	1029	18.4	10.5
G4	152	436	2.9	4.4	456	2473	15.5	25.2
G5	73	138	1.4	1.4	155	455	5.3	4.6
G6	59	105	1.1	1.1	107	292	3.6	3.0
G7	55	78	1.1	0.8	63	118	2.1	1.2
G8	55	83	1.1	0.8	61	152	2.1	1.6
G9	45	58	0.9	0.6	56	207	1.9	2.1
G10	34	61	0.7	0.6	40	42	1.4	0.4
Sum	4528	7614	87.7	76.8	2652	7037	90.2	71.8
Total	5166	9915	100	100	2940	9804	100	100

Also, the Centers' networks and the Department of Health's networks show different characteristics for COVID-19 (see Appendix Table 2 and Table 3). The most important difference between them is that the Centers shows people-based networks, whereas the Department of Health exhibits institute-based networks, as would be expected given their functions and scope of activities. For instance, twelve out of the top twenty accounts are for individuals in the Centers' networks, whereas the same number of institutes play a key role in the Department of Health's networks. This is probably attributable to the fact that the Centers consists of researchers to conduct research and provide information on diseases, whereas the Department of Health consists of institutes, such as the Centers, Food and Drug Administration, National Institutes of Health, and Health Resources and Services Administration. Many studies also find that offline networks play an important role in online networks (e.g., Dunbar et al., 2015; Huang et al., 2014; Kavanaugh et al., 2005; Li and Du, 2017; Reich et al., 2012). The Centers' and the Department of Health's organizational charts support the findings of this study and previous research (Figures 7 and 8). The Centers show the people-based networks, whereas the Department of Health exhibits the institutes-based networks in their offline networks. The finding highlights that offline social networks play an essential role in online social networks.

p883fig7 — Figure 7: The Centers' organizational chart (U.S. *Centers*..., 2020)

p883fig8 — Figure 8: The Department of Health's organizational chart (U.S. *Department*, 2020)

Limitations

This study has some limitations as follows: first, this study employed the NodeXL software between June 3 and June 10, 2020. Different time periods and different software might result in different results. Secondly, this study only analyses the social networks relating to COVID-19 on Twitter. Future research should explore other popular online platforms, such as Facebook, YouTube, or Instagram. Finally, this study only examines two government institutes in the USA.

Conclusions

This study shows five important results as follows:

This study finds that the Centers networks and the Department of Health networks show different characteristics for COVID-19. The Centers show people-based networks, whereas the Department of Health exhibits institutes-based networks. The result is consistent with their offline social networks. The finding highlights that offline social networks play an essential role in online social networks.
The Centers and the Department of Health have a different magnitude of importance as a key player role in each other's networks. For example, the Department of Health plays an essential role as the thirteenth key player in the Centers networks, whereas the Centers exert a significant impact on the networks as the second key player in the Department of Health networks. These differences exist because of the different mandates of the Centers and the Department of Health. To be specific, the Centers are the US federal agency that leads COVID-19 research, whereas the Department of Health is the cabinet-level executive branch department of the US federal government that relies on the COVID-19 research of the Centers.
Some key players play a pivotal role both in the Centers' and Department of Health networks. For instance, World Health Organization ranks third in the Centers' networks and eighteenth in the networks of the Department of Health.
Social networks of key players are differentiated by centrality types both in the Centers and the Department of Health networks. For example, they are in different places according to institutes and centrality types.
The Centers and the Department of Health show different characteristics of social networks according to groups. While group 1 plays a main role in the Centers' networks, many groups play an important role in the Department of Health networks.

The results are consistent with the expectation of this study, that is, the Centers and the Department of Health play a different information roles in COVID-19 and have different characteristics of social networks for COVID-19 that reflect the nature of their missions, range of activities and types of functioning.

This study also demonstrated functions of NodeXL, such as in-degree/out-degree/ betweenness centrality, cluster analysis and the Clauset-Newman-Moore cluster algorithm.

Acknowledgements

My thanks to the reviewers and the Regional Editor for their helpful suggestions.

About the author

Seungil Yum is a Ph.D. candidate at the University of Florida with interests in urban economics, economic development, transportation, culture, environment, big data analysis, social network, analysis, and creativity. He can be contacted at yumseungil@ufl.edu.

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How to cite this paper

Yum, S. (2020). Network relationships of U.S. government health agencies in relation to the Covid-19 pandemic. Information Research, 25(4), paper 883. Retrieved from http://InformationR.net/ir/25-4/paper883.html (Archived by the Internet Archive at https://bit.ly/3nze9mZ) https://doi.org/10.47989/irpaper883

Appendices

Appendix Table 1: Descriptive statistics
Graph metric	The Centers	The Department
Graph type	Directed	Directed
Maximum betweenness centrality	25826489	240
Total edges	12662	13672
Maximum edges in a connected component	12636	13663
Unique edges	9915	9804
Average betweenness centrality	6507	3
Vertices	5166	2940
Maximum vertices in a connected component	5141	2932
Maximum in-degree	4435	2091
Edges with duplicates	2747	3868
Self-loops	127	65
Maximum out-degree	46	240
Connected components	20	8
Single-vertex connected components	15	6
Maximum geodesic distance (Diameter)	4	5
Average geodesic distance	2.272	2.535
Average in-degree	2.073	3.595
Average out-degree	2.073	3.595
Median out-degree	2	3
Reciprocated edge ratio	0.004	0.010
Reciprocated vertex pair ratio	0.002	0.005
Graph density	0.000	0.001
Minimum in-degree	0	0
Median in-degree	0	0
Minimum out-degree	0	0
Minimum betweenness centrality	0	0
Median betweenness centrality	0	3

Appendix Table 2: Top twenty key players in the Centers for Disease Control and Prevention network
	Label	In-degree	Name	Description	Examples of tweets in social networks
1	CDC	4435	CDC @CDCgov	Centers for Disease Control and Prevention provide health considerations and tools for operating during COVID-19.	BREAKING @CDCgov just announced they're adopting key tenants of our racial data bill.
2	PM	628	Phil Murphy @GovMurphy	Governor of New Jersey.	Governor Murphy declared a state of emergency in New Jersey. If you think this is 'just the flu' – you are 100% wrong.
3	WHO	335	WHO @WHO	World Health Organization. WHO plays a leading role in COVID-19 response.	How complicit is the #WHO in #China's #coronavirus cover-up?
4	DT	195	Donald Trump @realdonaldtrump	The US president. President Trump declared a national emergency against COVID-19.	When will the American people have some real data on deaths due to Covid19?
5	QT	181	QuickTake by Bloomberg @quicktake	Global news network. Bloomberg provides the latest coronavirus news, including live updates, maps and ... Health authorities around the world are fighting a pandemic of Covid-19.	Despite initial data from China that showed elderly people and those with other health conditions were most vulnerable, young people in their 20s to early 40s are falling seriously ill from the #coronavirus.
6	DL	175	David Leopold @davidleopold	Immigration attorney. He posts many tweets about COVID-19 policies for immigrants.	Another Trump lie. Turns out More than a dozen US researchers, physicians & public health experts @CDCgov were working at @WHO's Geneva HQ when #COVID19 emerged last
7	ET	149	Eric Topol @erictopol	American cardiologist. He is the Founder and Director of the Scripps Research Translational Institute and discusses the evolving COVID-19 pandemic.	The @CDCgov used to be regarded as exemplary, the model emulated by other countries. What happened w/ #COVID19?
8	EF	145	Eric Feigl-Ding @drericding	American scientist, currently a Visiting Scientist in the Department of Nutrition at the Harvard T.H. Chan School of Public Health. He is immensely popular on Twitter for COVID-19.	Thailand study says #Covid19 patients can be infectious even after their symptomatic recovery.
9	JH	143	Jim Harris @jimharris	Disruptive Innovation Speaker. He was leader of the Green Party of Canada from 2003 to 2006. He argues that the COVID-19 crisis is accelerating digital transformation.	'Sweden chose to ignore the need for staying at home and social distancing'.
10	AP1	112	Ayanna Pressley @ayannapressley	U.S. Representative. She has served as the U.S. Representative for Massachusetts's 7th congressional district since 2019. She discusses the urgent need for direct, emergency aid to state and local governments to manage the COVID-19.	@AyannaPressley @CDCgov Latest CDC reporting on #COVID19 race & age breakdown.
11	NTD	93	NTD News @news_ntd	America's new independent TV channel. The news frequently focuses on Chinese people for COVID-19.	A doctor is voicing his concern that the @CDCgov guideline on how to complete #COVID19 death certificates may influence certifiers to inflate the number of deaths from the disease.
12	WH	91	The White House @whitehouse	The official workplace of the president of the US. The White House provides Coronavirus Guidelines for America.	How complicit is the #WHO in #China's #coronavirus cover-up?
13	HHS	86	HHS @HHSGov	The U.S. Department of Health and Human Services.	Our top priority with the #COVID19 response is to ensure critical supplies get to the right people at the right time, based on analysis by @HHSGov and @CDCgov.
14	PT	78	President Trump @potus	45th President of the US. President Trump declared a national emergency against COVID-19.	A scientific doc published nearly 10 yrs ago, Zinc inhibits #COVID19 activity & blocking its replication.
15	AP2	78	Congresswoman Ayanna Pressley @reppressley	U.S. Representative. She has served as the U.S. Representative for Massachusetts's 7th congressional district since 2019. She discusses the urgent need for direct, emergency aid to state and local governments to manage the COVID-19.	@CDCgov is acting on calls from me
16	MP	77	Mike Pence @vp	Vice President of the United States. In February 2020, Pence was appointed chairman of the White House Coronavirus Task Force, established in response to the COVID-19 pandemic in the United States	Trump and the Coronavirus Task Force needs to stop getting in front of the science. @VP
17	AA	76	Alex Azar @secazar	United States Secretary of Health and Human Services. Azar informed associates that he had alerted Trump on January 18, 2020, regarding the potential danger from COVID-19.	'Has the US lost its seat at the #GlobalHealth table in the midst of the #COVID19 response?' @SecAzar
18	SV	69	Seema Verma @seemacms	Administrator for the Centers for Medicare & Medicaid Services. She an American health policy consultant for COVID-19.	Nursing homes must now report to patients, their families and @CDCgov if there are cases of #COVID19 in their facilities, announces @SeemaCMS.
19	NYT	67	The New York Times @nytimes	American newspaper. The newspaper provides Live Covid-19 Updates and Tracker.	Thailand study says #Covid19 patients can be infectious even after their symptomatic recovery.
20	FDA	66	U.S. FDA @us_fda	Food and Drug Administration. The FDA is responsible for protecting and promoting public health against COVID-19.	The @CDCgov, @US_FDA, + @WHO all agree: tobacco use can put people at higher risk for severe illness from #COVID19.

Appendix Table 3: Top twenty key players in the Department of Health network
	Label	In-degree	Name	Description	Examples of tweets in social networks
1	HHS	2091	HHS @HHSGov	United States Department of Health and Human Services. the Department of Health works with state, local, tribal and territorial governments, public health officials, health care providers, researchers, private sector organizations and the public to execute a whole-of-America response to the COVID-19 pandemic.	Our top priority with the #COVID19 response is to ensure critical supplies get to the right people at the right time, based on analysis by @HHSGov and @CDCgov.
2	CDC	512	CDC @CDCgov	Centers for Disease Control and Prevention. CDC provides health considerations and tools for operating during COVID-19.	BREAKING @CDCgov just announced they're adopting key tenants of our racial data bill.
3	FEMA	495	FEMA @fema	Federal Emergency Management Agency. The agency's primary purpose is to coordinate the response to a disaster that has occurred in the United States, such as COVID-19.	We are working closely with @HHSGov to support states #COVID19 testing capabilities.
4	HS	308	Homeland Security @dhsgov	United States Department of Homeland Security. It is the U.S. federal executive department responsible for public security, including COVID-19.	It could be WORSE in the fall.
5	CBP	295	CBP @cbp	U.S. Customs and Border Protection. It is the country's primary border control organization against COVID-19.	@CBP @DHSgov @CBPArizona Affecting all detained-no one deserves #COVID19 as cases of ill+dying.
6	WH	254	The White House @whitehouse	The official workplace of the president of the US. The White House provides Coronavirus Guidelines for America.	How complicit is the #WHO in #China's #coronavirus cover-up?
7	AA	183	Alex Azar @secazar	United States Secretary of Health and Human Services.	Azar informed associates that he had alerted Trump on January 18, 2020, regarding the potential danger from COVID-19. Has the US lost its seat at the #GlobalHealth table in the midst of the #COVID19 response? @SecAzar
8	SG	179	U.S. Surgeon General @surgeon_general	The operational head of the U.S. Public Health Service. The U.S. surgeon general provides a lot of COVID-19 information.	NEW: @HHSGov through @HRSAgov awards $90M to support 581 #RyanWhite HIV/AIDS Program recipients in their efforts to prevent or minimize the impact of #COVID19 pandemic on people with #HIV:
9	DT	129	Donald Trump @realdonaldtrump	The US president. President Trump declared a national emergency against COVID-19.	When will the American people have some real data on deaths due to Covid19?
10	DD	105	Department of Defense @deptofdefense	Executive branch department of the federal government. The Department of Defense restricted all domestic and overseas travel to control COVID-19.	This is particularly important as we #StayHome during #COVID19.
11	JS	95	The Joint Staff @thejointstaff	Senior uniformed leaders in the United States Department of Defense. The Joint Staff discusses the Defense Department's response to the COVID-19 pandemic.	#GenMilley delivers an opening statement re current @DeptofDefense #COVID19 response efforts.
12	NIH	82	NIH @nih	National Institutes of Health. the National Institutes of Health releases resources and news regarding COVID-19.	Did you know Congress and their staff were getting daily briefings starting in January from @HHSGov @CDCgov @NIH and the @WhiteHouse about #COVID19?
13	AMA	78	AMA @amermedicalassn	American Medical Association. The association provides COVID-19 resource center for physicians.	We called on @HHSGov to ensure that physicians were included in the first round of CARES Act disbursements.
14	W	77	Walgreens @walgreens	The second-largest pharmacy store chain in the US. No-cost COVID-19 testing is available at select Walgreens locations.	Promises made...promises not kept. We will remember how @GOP & Trump Lied #COVID19 @Walgreens
15	HRSA	77	HRSA @hrsagov	Health Resources and Services Administration. It is the primary federal agency for improving access to health care services for COVID-19.	#StayHomeSaveLives
16	EJ	74	Eric Johnson @johnson4dallas	Mayor of Dallas. Eric Johnson encourages people to wear a mask and provides important news for COVID-19.	Good news: On April 10, I sent a letter to @HHSGov & @Walgreens urging them to select Dallas (particularly southern Dallas) for a new 15-minute #COVID19 testing site.
17	UB	62	Uché Blackstock @uche_blackstock	American physician. During the COVID-19 pandemic Blackstock used social media to share her experiences and concerns as a physician working on the front lines and on racial health disparities and inequities exposed by the pandemic.	Thank you to the Dutch news station @Nieuwsuur for providing a platform for me to amplify the #COVID19 racial disparities message and to continue the call for @HHSGov to release racial and ethnic demographic data on #COVID19 testing, cases and mortality.
18	WHO	62	WHO @WHO	World Health Organization. WHO plays a leading role in COVID-19 response.	How complicit is the #WHO in #China's #coronavirus cover-up?
19	SM	60	Steven Mnuchin @stevenmnuchin1	United States Secretary of the Treasury. Mnuchin testifies on federal response to COVID-19 and its impact on the economy.	@atrupar This is my idea—Put @realDonaldTrump's signature on the death certificate of every US victim of #COVID19.
20	CMS	59	CMSGov @cmsgov	The Centers for Medicare & Medicaid Services. CMSGov releases valuable information and updates about COVID-19.	Why are none of our medical/ gov organizations keeping track of #HCW #COVID19 exposure in a systematic way?