Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets.

BACKGROUND: The popularity of the magnetic vaccine conspiracy theory and other conspiracy theories of a similar nature creates challenges to promoting vaccines and disseminating accurate health information. OBJECTIVE: Health conspiracy theories are gaining in popularity. This study's objective was to evaluate the Twitter social media network related to the magnetic vaccine conspiracy theory and apply social capital theory to analyze the unique social structures of influential users. As a strategy for web-based public health surveillance, we conducted a social network analysis to identify the important opinion leaders sharing the conspiracy, the key websites, and the narratives. METHODS: A total of 18,706 tweets were retrieved and analyzed by using social network analysis. Data were retrieved from June 1 to June 13, 2021, using the keyword vaccine magnetic. Tweets were retrieved via a dedicated Twitter application programming interface. More specifically, the Academic Track application programming interface was used, and the data were analyzed by using NodeXL Pro (Social Media Research Foundation) and Gephi. RESULTS: There were a total of 22,762 connections between Twitter users within the data set. This study found that the most influential user within the network consisted of a news account that was reporting on the magnetic vaccine conspiracy. There were also several other users that became influential, such as an epidemiologist, a health economist, and a retired sports athlete who exerted their social capital within the network. CONCLUSIONS: Our study found that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own networks of Twitter followers. We emphasize the need for trust in influential users with regard to health information, particularly in the context of the widespread social uncertainty resulting from the COVID-19 pandemic, when public sentiment on social media may be unpredictable. This study highlights the potential of influential users to disrupt information flows of conspiracy theories via their unique social capital.

Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic.

Social media is not only an essential platform for the dissemination of public health-related information, but also an important channel for people to communicate during the COVID-19 pandemic. However, social bots can interfere with the social media topics that humans follow. We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online.

Does Social Media Users' Interaction Influence the Formation of Echo Chambers? Social Network Analysis Based on Vaccine Video Comments on YouTube.

The characteristics and influence of the echo chamber effect (TECE) of health misinformation diffusion on social media have been investigated by researchers, but the formation mechanism of TECE needs to be explored specifically and deeply. This research focuses on the influence of users' imitation, intergroup interaction, and reciprocity behavior on TECE based on the social contagion mechanism. A user comment-reply social network was constructed using the comments of a COVID-19 vaccine video on YouTube. The semantic similarity and Exponential Random Graph Model (ERGM) were used to calculate TECE and the effect of three interaction mechanisms on the echo chamber. The results show that there is a weak echo chamber effect (ECE) in the spread of misinformation about the COVID-19 vaccine. The imitation and intergroup interaction behavior are positively related to TECE. Reciprocity has no significant influence on TECE.

Advancing community-engaged research during the COVID-19 pandemic: Insights from a social network analysis of the trans-LINK Network.

Collaboration across sectors is critical to address complex health problems, particularly during the current COVID-19 pandemic. We examined the ability to collaborate during the pandemic as part of a baseline evaluation of an intersectoral network of healthcare and community organizations established to improve the collective response to transgender (trans) persons who have been sexually assaulted (the trans-LINK Network). A validated social network analysis survey was sent to 119 member organizations in Ontario, Canada. Survey respondents were asked, 'Has COVID-19 negatively affected your organization's ability to collaborate with other organizations on the support of trans survivors of sexual assault?' and 'How has COVID-19 negatively affected your organization's ability to collaborate within the trans-LINK Network?'. Data were analyzed using descriptive statistics. Seventy-eight member organizations participated in the survey (response rate = 66%). Most organizations (79%) indicated that the pandemic had affected their ability to collaborate with others in the network, citing most commonly, increased workload (77%), increased demand for services (57%), and technical and digital challenges (50%). Survey findings were shared in a stakeholder consultation with 22 representatives of 21 network member organizations. Stakeholders provided suggestions to prevent and address the challenges, barriers, and disruptions in serving trans survivors experienced during the pandemic, which were organized into themes. Seven themes were generated and used as a scaffold for the development of recommendations to advance the network, including: increase communication and knowledge exchange among member organizations through the establishment of a network discussion forum and capacity building group workshops; enhance awareness of network organizations by developing a member-facing directory of member services, their contributions, and ability to provide specific supports; strengthen capacity to provide virtual and in-person services and programs through enhanced IT support and increased opportunities for knowledge sharing and skill development; and adopt a network wide syndemic approach that addresses co-occurring epidemics (COVID-19 + racism, housing insecurity, transphobia, xenophobia) that impact trans survivors of sexual assault.

Investigating COVID-19 transmission in a tertiary hospital in Hanoi, Vietnam using social network analysis.

OBJECTIVES: In March 2020, a COVID-19 outbreak in a major referral hospital in Hanoi, Vietnam led to 7664 patients and staff being sent into lockdown for 2 weeks, and more than 52,200 persons across 49 provinces being quarantined. We assessed SARS-CoV-2 transmission patterns during this to-date largest hospital outbreak in Vietnam using social network analysis (SNA). METHODS: We constructed a directed relational network and calculated network metrics for 'degree', 'betweenness', 'closeness' and 'eigenvector' centrality to understand individual-level transmission patterns. We analysed network components and modularity to identify sub-network structures with disproportionately big effects. RESULTS: We detected 68 connections between 46 confirmed cases, of whom 27 (58.7%) were ancillary support staff, 7 (15.2%) caregivers, 6 (13%) patients and 2 (4.4%) nurses. Among the 10 most important cases selected by each SNA network metric, transmission dynamics clustered in 17 cases, of whom 12 (70.6%) cases were ancillary support staff. Ancillary support staff also constituted 71.1% of cases in the dominant sub-network and 68.8% of cases in the three largest sub-communities. CONCLUSIONS: We identified non-clinical ancillary support staff, who are responsible for room service and food distribution in hospital wards in Vietnam, as a group with disproportionally big impacts on transmission dynamics during this outbreak. Our findings call for a holistic approach to nosocomial outbreak prevention and response that includes both clinical and non-clinical hospital staff. Our work also shows the potential of SNA as a complementary outbreak investigation method to better understand infection patterns in hospitals and similar settings.

COVID-19's impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis.

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students' online learning behavior before and after the outbreak. We collected review data from China's massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses' interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic's role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Understanding organ donation processes and structures in ontario: A social network analysis approach.

BACKGROUND: Transplant rates in Ontario rose steeply in the decade prior to the COVID-19 pandemic. Reasons for that increase remain unclear, but the inter-organizational arrangement of organ donation programs may have contributed. However, there is a paucity of literature investigating these inter-organizational arrangements, with a limited understanding of how communication facilitates organ donation. Understanding these arrangements may help to re-establish rising organ donation rates post-pandemic. OBJECTIVE: To describe interprofessional interactions of Organ and Tissue Donation Coordinators (OTDCs) during organ donation cases, within organ donation programs in Ontario, from an organizational perspective (describing structure, context, process). METHODS: Mixed-method social network analysis (SNA) approach analyzing 14 organ donation cases just before the COVID-19 pandemic. RESULTS: Structure: Social network graphs depict the joint work performed by hospital staff and OTDCs, with a great part of the communication being processed through the OTDC. CONTEXT: Network density ranged from 0.05 to 0.24 across cases, and health care professionals perceived an atmosphere of shared vision and trust among team members. PROCESS: Most networks had a degree centralization <0.50 suggesting a decentralized information flow, and participants perceived decisions being jointly made. The characteristic path length of cases ranged from 1.6 to 3.2, suggesting potential for rapid information diffusion. Overall, data reinforced the OTDC role of intermediator within the communication process, and hospital staff perceived OTDCs as central players. Hospital staff and OTDCs reported frustration with some aspects of the flow of information during the organ allocation processes. CONCLUSION: Findings from this study provide a network map of communications within organ donation cases and reinforce the importance of the OTDC role. Opportunities for quality improvement within these processes are identified.

Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong.

BACKGROUND: Domestic workers, flight crews, and sailors are three vulnerable population subgroups who were required to travel due to occupational demand in Hong Kong during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to explore the social networks among three vulnerable population subgroups and capture temporal changes in their probability of being exposed to SARS-CoV-2 via mobility. METHODS: We included 652 COVID-19 cases and utilized Exponential Random Graph Models to build six social networks: one for the cross-sectional cohort, and five for the temporal wave cohorts, respectively. Vertices were the three vulnerable population subgroups. Edges were shared scenarios where vertices were exposed to SARS-CoV-2. RESULTS: The probability of being exposed to a COVID-19 case in Hong Kong among the three vulnerable population subgroups increased from 3.38% in early 2020 to 5.78% in early 2022. While domestic workers were less mobile intercontinentally compared to flight crews and sailors, domestic workers were 1.81-times in general more likely to be exposed to SARS-CoV-2. CONCLUSIONS: Vulnerable populations with similar ages and occupations, especially younger domestic workers and flight crew members, were more likely to be exposed to SARS-CoV-2. Social network analysis can be used to provide critical information on the health risks of infectious diseases to vulnerable populations.

Social Network Analysis of COVID-19 Sentiments: 10 Metropolitan Cities in Italy.

The pandemic spread rapidly across Italy, putting the region's health system on the brink of collapse, and generating concern regarding the government's capacity to respond to the needs of patients considering isolation measures. This study developed a sentiment analysis using millions of Twitter data during the first wave of the COVID-19 pandemic in 10 metropolitan cities in Italy's (1) north: Milan, Venice, Turin, Bologna; (2) central: Florence, Rome; (3) south: Naples, Bari; and (4) islands: Palermo, Cagliari. Questions addressed are as follows: (1) How did tweet-related sentiments change over the course of the COVID-19 pandemic, and (2) How did sentiments change when lagged with policy shifts and/or specific events? Findings show an assortment of differences and connections across Twitter sentiments (fear, anger, and joy) based on policy measures and geographies during the COVID-19 pandemic. Results can be used by policy makers to quantify the satisfactory level of positive/negative acceptance of decision makers and identify important topics related to COVID-19 policy measures, which can be useful for imposing geographically varying lockdowns and protective measures using historical data.

Korea's Response to COVID-19 According to Set Time Frames, With a Focus on the Network Between the Government and Responding Agencies: Social Network Analysis.

BACKGROUND: In December 2019, COVID-19 was first confirmed in Wuhan, China, and as the respiratory disease spread around the globe, there was a spike in interest worldwide in combating such contagious diseases. When such disasters occur, the central government of South Korea and its affiliated local governments-together with nongovernmental organizations-play a crucial role in crisis management systems. OBJECTIVE: The purpose of this paper is to corroborate the characteristics government ministries and domestic and foreign institutions exhibit through their interconnection when the parties are undergoing a disease-related catastrophe such as the COVID-19 pandemic. METHODS: Using the social network analysis technique, the span of the COVID-19 pandemic was segmented into 3 time frames, and the relational characteristics of the COVID-19 contagious disease response department and related agencies at home and abroad were analyzed based on 3 centralities. RESULTS: Evidence from the second and third time frames indicates that the agents reacting to contagious diseases do not necessarily hold the central position in the network. From this, it can be inferred that it is not only the primary host that plays a pivotal role but the key to a successful response to various disasters also lies in cooperation with the relevant parties. CONCLUSIONS: The incongruency between the findings of this paper and the existing disaster response system gives rise to the corollary that both the essential parties and the adjoining ones need to collaborate for a coordinated crisis response in disaster situations. Furthermore, much significance lies in the fact that this paper explores the various aspects that could surface among the host and relevant parties in a real-life pandemic.

A Social Network Analysis of Twitter Data Related to Blood Clots and Vaccines.

After the first weeks of vaccination against SARS-CoV-2, several cases of acute thrombosis were reported. These news reports began to be shared frequently across social media platforms. The aim of this study was to conduct an analysis of Twitter data related to the overall discussion. The data were retrieved from 14 March to 14 April 2021 using the keyword 'blood clots'. A dataset with n = 266,677 tweets was retrieved, and a systematic random sample of 5% of tweets (n = 13,334) was entered into NodeXL for further analysis. Social network analysis was used to analyse the data by drawing upon the Clauset-Newman-Moore algorithm. Influential users were identified by drawing upon the betweenness centrality measure. Text analysis was applied to identify the key hashtags and websites used at this time. More than half of the network comprised retweets, and the largest groups within the network were broadcast clusters in which a number of key users were retweeted. The most popular narratives involved highlighting the low risk of obtaining a blood clot from a vaccine and highlighting that a number of commonly consumed medicine have higher blood clot risks. A wide variety of users drove the discussion on Twitter, including writers, physicians, the general public, academics, celebrities, and journalists. Twitter was used to highlight the low potential of developing a blood clot from vaccines, and users on Twitter encouraged vaccinations among the public.

Identification of cohesive subgroups in a university hall of residence during the COVID-19 pandemic using a social network analysis approach.

THE AIMS: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students have experienced significant changes in their relationships, especially in the halls of residence. Previous research has shown the importance of relationship structure in contagion processes. However, there is a lack of studies in the university setting, where students live closely together. The case study methodology was applied to carry out a descriptive study. The participation consisted of 43 university students living in the same hall of residence. Social network analysis has been applied for data analysis. Factions and Girvan-Newman algorithms have been applied to detect the existing cohesive subgroups. The UCINET tool was used for the calculation of the SNA measure. A visualization of the global network will be carried out using Gephi software. After applying the Girvan-Newman and Factions, in both cases it was found that the best division into subgroups was the one that divided the network into 4 subgroups. There is high degree of cohesion within the subgroups and a low cohesion between them. The relationship between subgroup membership and gender was significant. The degree of COVID-19 infection is related to the degree of clustering between the students. College students form subgroups in their residence. Social network analysis facilitates an understanding of structural behavior during the pandemic. The study provides evidence on the importance of gender, race and the building where they live in creating network structures that favor, or not, contagion during a pandemic.

COVID-19 pandemic and food poverty conversations: Social network analysis of Twitter data.

This novel and mixed-method study investigated food poverty conversations at the beginning of the COVID-19 pandemic and the subsequent national lockdown on the social media platform Twitter. NodeXL Pro software was used to collect tweets using the terms 'food' and 'poverty' in any order somewhere in a tweet sent on selected days between April 5 and May 23, 2020. The data obtained from NodeXL Pro were cleaned. Social network analysis tools were used to analyse and visualise our data. Using this method, sentiment-related words (positive or negative words), the top (the most mentioned) 10 hashtags, top words and top word pairs were identified. The patterns of word pairs communicated in our network were visualised based on each word pair's frequency. This also enabled us to carry out a content analysis to create coding of the word pairs' data. A total of 81 249 tweets were identified that contained the terms 'food' and 'poverty'. Our findings revealed that individuals' tweets overwhelmingly contained views about the increase in hunger, food poverty and food insecurity due to the COVID-19 pandemic. Twitter users perceived that when the pandemic measures began, many food-secure families were pushed into food insecurity due to a rapid rise in unemployment and rising poverty due to the quarantine and stay-at-home instructions in place at the time. They also addressed the sharp rise in food poverty being driven by panic buying, food shortages, food affordability and disruptions in food supply and food systems. Our analysis of this data suggests that to mitigate food poverty or to prevent a 'hunger pandemic' for future pandemic emergencies, comprehensive and longer term policy responses and economic supports are needed to strengthen the resilience of food systems. However, the highlighted limitations of this study must be considered.

The dramatic increase in anti-vaccine discourses during the COVID-19 pandemic: a social network analysis of Twitter.

BACKGROUND/AIM: The first case of COVID-19 in Turkey was officially recorded on March 11, 2020. Social media use increased worldwide, as well as in Turkey, during the pandemic, and conspiracy theories/fake news about medical complications of vaccines spread throughout the world. The aim of this study was to identify community interactions related to vaccines and to identify key influences/influencers before and after the pandemic using social network data from Twitter. MATERIALS AND METHODS: Two datasets, including tweets about vaccinations before and after COVID-19 in Turkey, were collected. Social networks were created based on interactions (mentions) between Twitter users. Users and their influence were scored based on social network analysis and parameters that included in-degree and betweenness centrality. RESULTS: In the pre-COVID-19 network, media figures and authors who had anti-vaccine views were the most influential users. In the post-COVID-19 network, the Turkish minister of health, the was the most influential figure. The vaccine network was observed to be growing rapidly after COVID-19, and the physicians and authors who had opinions about mandatory vaccinations received a great deal of reaction. One-way communication between influencers and other users in the network was determined. CONCLUSIONS: This study shows the effectiveness and usefulness of large social media data for understanding public opinion on public health and vaccination in Turkey. The current study was completed before the implementation of the COVID-19 vaccine in Turkey. We anticipated that social network analysis would help reduce the "infodemic" before administering the vaccine and would also help public health workers act more proactively in this regard.

Contact network analysis of Covid-19 in tourist areas--Based on 333 confirmed cases in China.

The spread of infectious diseases is highly related to the structure of human networks. Analyzing the contact network of patients can help clarify the path of virus transmission. Based on confirmed cases of COVID-19 in two major tourist provinces in southern China (Hainan and Yunnan), this study analyzed the epidemiological characteristics and dynamic contact network structure of patients in these two places. Results show that: (1) There are more female patients than males in these two districts and most are imported cases, with an average age of 45 years. Medical measures were given in less than 3 days after symptoms appeared. (2) The whole contact network of the two areas is disconnected. There are a small number of transmission chains in the network. The average values of degree centrality, betweenness centrality, and PageRank index are small. Few patients have a relatively high contact number. There is no superspreader in the network.

Reconstructing a COVID-19 outbreak within a religious group using social network analysis simulation in Korea.

OBJECTIVES: We reconstructed a coronavirus disease 2019 (COVID-19) outbreak to examine how a large cluster at a church setting spread before being detected and estimate the potential effectiveness of complying with mask-wearing guidelines recommended by the government. METHODS: A mathematical model with a social network analysis (SNA) approach was used to simulate a COVID-19 outbreak. A discrete-time stochastic simulation model was used to simulate the spread of COVID-19 within the Sarang Jeil church. A counterfactual experiment using a calibrated baseline model was conducted to examine the potential benefits of complying with a mask-wearing policy. RESULTS: Simulations estimated a mask-wearing ratio of 67% at the time of the outbreak, which yielded 953.8 (95% confidence interval [CI], 937.3 to 970.4) cases and was most consistent with the confirmed data. The counterfactual experiment with 95% mask-wearing estimated an average of 45.6 (95% CI, 43.4 to 47.9) cases with a standard deviation of 20.1. The result indicated that if the church followed government mask-wearing guidelines properly, the outbreak might have been one-twentieth the size. CONCLUSIONS: SNA is an effective tool for monitoring and controlling outbreaks of COVID-19 and other infectious diseases. Although our results are based on simulations and are thus limited, the precautionary implications of social distancing and mask-wearing are still relevant. Since person-to-person contacts and interactions are unavoidable in social and economic life, it may be beneficial to develop precise measures and guidelines for particular organizations or places that are susceptible to cluster outbreaks.

Modeling of network based digital contact tracing and testing strategies, including the pre-exposure notification system, for the COVID-19 pandemic.

With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The model uses a real-world social network generated from a high-resolution contact data set of 180 students. This model incorporates infectivity variations, test sensitivities, incubation period, and asymptomatic cases. We present a method to extend the weighted temporal social network and present simulations on a network of 5000 students. The purpose of this work is to investigate optimal quarantine rules and testing strategies with digital contact tracing. The results show that the traditional strategy of quarantining direct contacts reduces infections by less than 20% without sufficient testing. Periodic testing every 2 weeks without contact tracing reduces infections by less than 3%. A variety of strategies are discussed including testing second and third degree contacts and the pre-exposure notification system, which acts as a social radar warning users how far they are from COVID-19. The most effective strategy discussed in this work was combining the pre-exposure notification system with testing second and third degree contacts. This strategy reduces infections by 18.3% when 30% of the population uses the app, 45.2% when 50% of the population uses the app, 72.1% when 70% of the population uses the app, and 86.8% when 95% of the population uses the app. When simulating the model on an extended network of 5000 students, the results are similar with the contact tracing app reducing infections by up to 79%.

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic.

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

[Topic Modeling and Keyword Network Analysis of News Articles Related to Nurses before and after "the Thanks to You Challenge" during the COVID-19 Pandemic].

PURPOSE: This study was conducted to assess public awareness and policy challenges faced by practicing nurses. METHODS: After collecting nurse-related news articles published before and after 'the Thanks to You Challenge' campaign (between December 31, 2019, and July 15, 2020), keywords were extracted via preprocessing. A three-step method keyword analysis, latent Dirichlet allocation topic modeling, and keyword network analysis was used to examine the text and the structure of the selected news articles. RESULTS: Top 30 keywords with similar occurrences were collected before and after the campaign. The five dominant topics before the campaign were: pandemic, infection of medical staff, local transmission, medical resources, and return of overseas Koreans. After the campaign, the topics 'infection of medical staff' and 'return of overseas Koreans' disappeared, but 'the Thanks to You Challenge' emerged as a dominant topic. A keyword network analysis revealed that the word of nurse was linked with keywords like thanks and campaign, through the word of sacrifice. These words formed interrelated domains of 'the Thanks to You Challenge' topic. CONCLUSION: The findings of this study can provide useful information for understanding various issues and social perspectives on COVID-19 nursing. The major themes of news reports lagged behind the real problems faced by nurses in COVID-19 crisis. While the press tends to focus on heroism and whole society, issues and policies mutually beneficial to public and nursing need to be further explored and enhanced by nurses.

Network centrality for the identification of biomarkers in respondent-driven sampling datasets.

Networks science techniques are frequently used to provide meaningful insights into the populations underlying medical and social data. This paper examines SATHCAP, a dataset related to HIV and drug use in three US cities. In particular, we use network measures such as betweenness centrality, closeness centrality, and eigenvector centrality to find central, important nodes in a network derived from SATHCAP data. We evaluate the attributes of these important nodes and create an exceptionality score based on the number of nodes that share a particular attribute. This score, along with the underlying network itself, is used to reveal insight into the attributes of groups that can be effectively targeted to slow the spread of disease. Our research confirms a known connection between homelessness and HIV, as well as drug abuse and HIV, and shows support for the theory that individuals without easy access to transportation are more likely to be central to the spread of HIV in urban, high risk populations.