Heavy-tailed sexual contact networks and monkeypox epidemiology in the global outbreak, 2022

The outbreak of monkeypox across non-endemic regions confirmed in May 2022 shows epidemiological features distinct from previously imported outbreaks, most notably its observed growth and predominance amongst men who have sex with men (MSM). We use a transmission model fitted to empirical sexual partnership data to show that the heavy-tailed sexual partnership distribution, in which a handful of individuals have disproportionately many partners, can explain the sustained growth of monkeypox among MSM despite the absence of such patterns previously. We suggest that the basic reproduction number (R0) for monkeypox over the MSM sexual network may be substantially above 1, which poses challenges to outbreak containment. Ensuring support and tailored messaging to facilitate prevention and early detection among MSM with high numbers of partners is warranted.

Nursing home staff networks and COVID-19

Nursing homes and other long-term care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in US nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, WA, to other skilled nursing facilities. The full extent of staff connections between nursing homes-and the role these connections serve in spreading a highly contagious respiratory infection-is currently unknown given the lack of centralized data on cross-facility employment. We perform a large-scale analysis of nursing home connections via shared staff and contractors using device-level geolocation data from 50 million smartphones, and find that 5.1% of smartphone users who visited a nursing home for at least 1 h also visited another facility during our 11-wk study period-even after visitor restrictions were imposed. We construct network measures of connectedness and estimate that nursing homes, on average, share connections with 7.1 other facilities. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Controlling for demographic and other factors, a home's staff network connections and its centrality within the greater network strongly predict COVID-19 cases.

Dimensions of Misinformation About the HPV Vaccine on Instagram: Content and Network Analysis of Social Media Characteristics

BACKGROUND

The human papillomavirus (HPV) vaccine is a major advancement in cancer prevention and this primary prevention tool has the potential to reduce and eliminate HPV-associated cancers; however, the safety and efficacy of vaccines in general and the HPV vaccine specifically have come under attack, particularly through the spread of misinformation on social media. The popular social media platform Instagram represents a significant source of exposure to health (mis)information; 1 in 3 US adults use Instagram.

OBJECTIVE

The objective of this analysis was to characterize pro- and anti-HPV vaccine networks on Instagram, and to describe misinformation within the anti-HPV vaccine network.

METHODS

From April 2018 to December 2018, we collected publicly available English-language Instagram posts containing hashtags #HPV, #HPVVaccine, or #Gardasil using Netlytic software (n=16,607). We randomly selected 10% of the sample and content analyzed relevant posts (n=580) for text, image, and social media features as well as holistic attributes (eg, sentiments, personal stories). Among antivaccine posts, we organized elements of misinformation within four broad dimensions: 1) misinformation theoretical domains, 2) vaccine debate topics, 3) evidence base, and 4) health beliefs. We conducted univariate, bivariate, and network analyses on the subsample of posts to quantify the role and position of individual posts in the network.

RESULTS

Compared to provaccine posts (324/580, 55.9%), antivaccine posts (256/580, 44.1%) were more likely to originate from individuals (64.1% antivaccine vs 25.0% provaccine; P<.001) and include personal narratives (37.1% vs 25.6%; P=.003). In the antivaccine network, core misinformation characteristics included mentioning #Gardasil, purporting to reveal a lie (ie, concealment), conspiracy theories, unsubstantiated claims, and risk of vaccine injury. Information/resource posts clustered around misinformation domains including falsification, nanopublications, and vaccine-preventable disease, whereas personal narrative posts clustered around different domains of misinformation, including concealment, injury, and conspiracy theories. The most liked post (6634 likes) in our full subsample was a positive personal narrative post, created by a non-health individual; the most liked post (5604 likes) in our antivaccine subsample was an informational post created by a health individual.

CONCLUSIONS

Identifying characteristics of misinformation related to HPV vaccine on social media will inform targeted interventions (eg, network opinion leaders) and help sow corrective information and stories tailored to different falsehoods.

E-cigarette brands and social media influencers on Instagram: a social network analysis

BACKGROUND

Exposure to visual posts featuring e-cigarette products on social media is associated with increased e-cigarette use among US adolescents. Instagram is the largest source of e-cigarette social media marketing, where influencers-for example, bloggers, brand ambassadors-post promotional materials. This study analysed the network of e-cigarette brands and influencers on Instagram, characterising the most central players in e-cigarette social media marketing.

METHODS

We tracked influencers with public profiles on Instagram who posted promotional e-cigarette content in 2020, had over 1000 followers and high user engagement rate (ratio of likes and comments to followers) of 1%-25% per post. By conducting a social network analysis, we identified the most central (highly involved in promotional activities) influencers and e-cigarette brands. The number of the influencers' followers aged 13-17 years old and the age verification practices restricting youth access were also assessed.

RESULTS

There is a highly interconnected network of engaging e-cigarette influencers (n=55) worldwide who collaborated with over 600 e-cigarette brands in 2020. The Asian and US influencers had five to six times more teenage followers compared with the European influencers. 75% of the influencers did not restrict youth access to their promotional content on Instagram. The brands Voopotech, Innokin, Geekvape, Lost Vape, Smok and Vaporesso collaborated with the largest number of influencers (mean n=20).

CONCLUSIONS

It is important to understand associations among influencers and e-cigarette use behaviours, especially youth, to inform effective public health communication and potential policies that could regulate social media marketing sponsored by e-cigarette companies.

Meeting in the Middle: The Role of Mutual Biracial Friends in Cross-Race Friendships

Social network analysis was used to examine the role of having a mutual biracial friend on cross-race friendship nominations among monoracial sixth-grade students (Mage  = 10.56 years) in two racially diverse middle schools (n = 385; n = 351). Monoracial youth were most likely to choose same-race peers as friends but more likely to choose biracial than different-race peers as friends, suggesting that racial homophily may operate in an incremental way to influence friendships. Monoracial different-race youth were also more likely to be friends if they had a mutual biracial friend. The findings shed light on the unique role that biracial youth play in diverse friendship networks. Implications for including biracial youth in studies of cross-race friendship are discussed.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.

A Pragmatic Randomized Controlled Trial to Increase PrEP Uptake for HIV Prevention: 55-Week Results From PrEPChicago

OBJECTIVES

We tested preliminary efficacy of a peer change agent type I network intervention to increase pre-exposure prophylaxis (PrEP) linkage to care among network members connected to young Black men who have sex with men.

DESIGN

Parent study is a pragmatic randomized controlled trial with 110 weeks of total follow-up. Interim midpoint analyses are performed here using participant data before crossover assignment at 55 weeks.

METHODS

We randomly assigned 423 participants in Chicago to receive the network intervention, an opinion leader workshop with telephonic booster sessions, versus a time-matched control from 2016 to 2018. The consolidated surrogate outcome was PrEP referral and linkage to clinical care among network members connected to study participants and was collected from independent administrative data.

RESULTS

Each study participant in the trial (n = 423) had on average 1822 network contacts who could be eligible for PrEP referral and linkage. During the 55-week observation period, PrEP referral was most likely to occur within 3 days of an intervention session compared to control [odds ratio (OR) 0.07 (0.02-0.013); P = 0.007] resulting in 1-2 referrals of network members per session. Network members with referral or linkage were more likely to be connected to study participants in the intervention arm than the control condition [aOR 1.50 (1.09-2.06); P = 0.012].

CONCLUSIONS

A peer change agent type I network intervention is preliminarily effective at diffusing PrEP through a network of individuals highly susceptible to HIV over 55 weeks. This low-intensity intervention demonstrated network-level impact among populations that have experienced limited PrEP care engagement in the United States.

A social network analysis of the spread of COVID-19 in South Korea and policy implications

This study estimates the COVID-19 infection network from actual data and draws on implications for policy and research. Using contact tracing information of 3283 confirmed patients in Seoul metropolitan areas from January 20, 2020 to July 19, 2020, this study created an infection network and analyzed its structural characteristics. The main results are as follows: (i) out-degrees follow an extremely positively skewed distribution; (ii) removing the top nodes on the out-degree significantly decreases the size of the infection network, and (iii) the indicators that express the infectious power of the network change according to governmental measures. Efforts to collect network data and analyze network structures are urgently required for the efficiency of governmental responses to COVID-19. Implications for better use of a metric such as R0 to estimate infection spread are also discussed.

Value of social network analysis for developing and evaluating complex healthcare interventions: a scoping review

OBJECTIVES

Most complex healthcare interventions target a network of healthcare professionals. Social network analysis (SNA) is a powerful technique to study how social relationships within a network are established and evolve. We identified in which phases of complex healthcare intervention research SNA is used and the value of SNA for developing and evaluating complex healthcare interventions.

METHODS

A scoping review was conducted using the Arksey and O'Malley methodological framework. We included complex healthcare intervention studies using SNA to identify the study characteristics, level of complexity of the healthcare interventions, reported strengths and limitations, and reported implications of SNA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews 2018 was used to guide the reporting.

RESULTS

Among 2466 identified studies, 40 studies were selected for analysis. At first, the results showed that SNA seems underused in evaluating complex intervention research. Second, SNA was not used in the development phase of the included studies. Third, the reported implications in the evaluation and implementation phase reflect the value of SNA in addressing the implementation and population complexity. Fourth, pathway complexity and contextual complexity of the included interventions were unclear or unable to access. Fifth, the use of a mixed methods approach was reported as a strength, as the combination and integration of a quantitative and qualitative method clearly establishes the results.

CONCLUSION

SNA is a widely applicable method that can be used in different phases of complex intervention research. SNA can be of value to disentangle and address the level of complexity of complex healthcare interventions. Furthermore, the routine use of SNA within a mixed method approach could yield actionable insights that would be useful in the transactional context of complex interventions.

Autistic Symptoms and Social Functioning in Psychosis: A Network Approach

Psychotic and autistic symptoms are related to social functioning in individuals with psychotic disorders (PD). The present study used a network approach to (1) evaluate the interactions between autistic symptoms, psychotic symptoms, and social functioning, and (2) investigate whether relations are similar in individuals with and without PD. We estimated an undirected network model in a sample of 504 PD, 572 familial risk for psychosis (FR), and 337 typical comparisons (TC), with a mean age of 34.9 years. Symptoms were assessed with the Autism Spectrum Quotient (AQ; 5 nodes) and the Community Assessment of Psychic Experiences (CAPE; 9 nodes). Social functioning was measured with the Social Functioning Scale (SFS; 7 nodes). We identified statistically significant differences between the FR and PD samples in global strength (P < .001) and network structure (P < .001). Our results show autistic symptoms (social interaction nodes) are negatively and more closely related to social functioning (withdrawal, interpersonal behavior) than psychotic symptoms. More and stronger connections between nodes were observed for the PD network than for FR and TC networks, while the latter 2 were similar in density (P = .11) and network structure (P = .19). The most central items in strength for PD were bizarre experiences, social skills, and paranoia. In conclusion, specific autistic symptoms are negatively associated with social functioning across the psychosis spectrum, but in the PD network symptoms may reinforce each other more easily. These findings emphasize the need for increased clinical awareness of comorbid autistic symptoms in psychotic individuals.

Synergistic CO2 reduction effects in Chinese urban agglomerations: Perspectives from social network analysis

China has released its ambitious target for carbon neutrality by 2060. With decades of top-down energy conservation and pollutant mitigation policies, the techno-mitigation space has gradually shrunk, while more mitigation space is required for a systematic approach. To help to uncover CO₂ mitigation effects, location and better pathways from a systematic perspective, this paper combines disparity analysis and social network analysis to investigate the synergistic emissions reduction effect of urban agglomerations in three representative Chinese urban agglomerations, namely the Yangtze River Delta urban agglomeration (YRD), Chengdu-Chongqing urban agglomeration (CY) and Guangdong-Hong Kong-Macao urban agglomeration (GHM). Based on understanding of the carbon emission disparity characteristics of the three urban agglomerations using disparity analysis, this study uses social network analysis to study the synergistic CO₂ reductions in each urban agglomeration from three perspectives: overall, individual, and connection. The findings emphasize that CY presented the greatest synergistic development capacity but with weak driving ability, indicating that overall synergistic emission reduction was difficult to achieve in a short period. GHM presented obvious fragmentation between the core and peripheral cities, resulting in a weak synergistic mitigation effect. YRD highlighted a solid synergistic development capacity with strong driving ability by its developed cities, thus generating the greatest potential to reduce CO₂ emissions in the short and middle terms. Different cities assume different roles in synergistic CO₂ reduction. Our results can be expected to enlighten more regionally oriented CO₂ mitigation policy implications from an urban agglomeration perspective.

Relationships between childhood trauma and perceived stress in the general population: a network perspective

BACKGROUND

Experiences of childhood trauma (CT) are associated with increased psychological vulnerability. Past research suggests that CT might alter stress processing with a subsequent negative impact on mental health. However, it is currently unclear how different domains of CT exert effects on specific subjective experiences of stress during adulthood.

METHODS

In the present study, we used network analysis to explore the complex interplay between distinct domains of CT and perceived stress in a large, general-population sample of middle-aged adults (N = 1252). We used a data-driven community-detection algorithm to identify strongly connected subgroups of items within the network. To assess the replicability of the findings, we repeated the analyses in a second sample (N = 862). Combining data from both samples, we evaluated network differences between men (n = 955) and women (n = 1159).

RESULTS

Results indicate specific associations between distinct domains of CT and perceived stress. CT domains reflecting a dimension of deprivation, i.e. experiences of neglect, were associated exclusively to a stress network community representing low perceived self-efficacy. By contrast, CT associated with threat, i.e. experiences of abuse, was specifically related to a stress community reflecting perceived helplessness. Our results replicated with high accordance in the second sample. We found no difference in network structure between men and women, but overall a stronger connected network in women.

CONCLUSIONS

Our findings emphasize the unique role of distinct domains of CT in psychological stress processes in adulthood, implying opportunities for targeted interventions following distinct domains of CT.

The relationship between childhood adversity and adult personality revealed by network analysis

BACKGROUND

Childhood adversity is known to influence personality development. Studies suggest that distinct types of childhood adversities have differential effects on personality dimensions. However, different types of adversity often co-occur, and personality dimensions are strongly interconnected.

OBJECTIVE

The aim of this study was to use a network approach to analyze the interrelationships between different types of childhood adversity and diverse personality dimensions integratively.

PARTICIPANTS AND SETTING

We used previously collected data on 142 alcohol dependent patients and 102 healthy controls.

METHODS

The participants completed the Interview for Traumatic Events in Childhood, the Parental Acceptance and Rejection Questionnaire and the Temperament and Character Inventory. Outcomes on the subscales of these instruments were included in the network analysis.

RESULTS

The resulting network showed strong connections between different types of childhood adversity, and between the different temperaments and characters of personality. Childhood adversity, mainly physical abuse and maternal rejection, was connected to most personality dimensions, except for reward dependence. Physical abuse showed the highest centrality measures, indicating a central, important role in the network.

CONCLUSIONS

These findings confirm that different types of childhood adverse experiences often co-occur, and suggest that specifically physical and emotional abuse, and maternal rejection might play a prominent role in shaping personality.

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

OBJECTIVES

The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes.

MATERIALS AND METHODS

A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared.

RESULTS

Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists.

DISCUSSION

The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay.

CONCLUSIONS

Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

Characterization of relationship instability in women with borderline personality disorder: A social network analysis

Interpersonal dysfunction is considered a cornerstone of borderline personality disorder (BPD). Relationships are described as intense and unstable, with individuals with BPD alternating between idealization and devaluation of relationship partners. Furthermore, a lack of stable and supportive relationships may be related to symptom maintenance and exacerbation. Despite widespread recognition of the importance of relationship instability in BPD, there is little empirical evidence about the nature of such instability and how it emerges over time. We examined the stability of social networks of women diagnosed with BPD (n = 27) and healthy controls (HCs; n = 23) by assessing key characteristics of relationships (satisfaction, support, closeness, conflict, and criticism) over a 6-month period. We conducted analyses to examine whether relationship instability depended on the frequency of interaction with members of the network. Results showed that the relationships of women in the BPD group were perceived as more unstable than those of the HC group. Compared with women in the HC group, women with BPD had networks with more relationships that had undergone significant change or had been "cut off" over the course of the study. The relationship between frequency of interaction and instability in support and satisfaction differed between groups. Women in the HC group showed greater instability in support with partners they interacted with infrequently, whereas women in the BPD group showed greater instability in satisfaction with partners they interacted with more frequently. Implications for understanding interpersonal dysfunction in BPD are discussed and possible areas of relevance for treatment development are highlighted. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method

Clarifying the spatial association network of provincial building carbon emissions and its influential drivers is profoundly significant for transregional collaborative emission reduction and regionally-coordinated development. This study adopts the social network analysis method to investigate the network structure characteristics of carbon emissions in the building sector based on China's provincial-level evidence from 2000 to 2018. Then, the quadratic assignment procedure is further utilized to examine the driving factors. The results demonstrate that building carbon emissions in China take the form of a network structure. From 2000 to 2018, the relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang are in the center of the spatial association network and play a vital part in the network. The network of carbon emissions in the building sector can be classified into four plates: the main inflow plate, main outflow plate, bidirectional spillover plate and agent plate. Geographical adjacency, economic development level, energy intensity and industrial structure are significantly correlated with building carbon emissions. The urbanization level has no significant influence on the spatial correlations of building carbon emissions. This study is conducive to formulating energy conservation policies and promoting transregional collaborative emission reductions.

Human mobility networks reveal increased segregation in large cities

A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1-6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city's hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.

Financing of physical rehabilitation services in Iran: a stakeholder and social network analysis

BACKGROUND

Inadequate financing is one of the major barriers in securing equitable access to high-quality physical rehabilitation services, without imposing financial hardship. Despite this, no sufficient attention has been paid to physical rehabilitation services and no specific financial resources have been allocated to such services in many countries including Iran. Owing to the fact that effective decision- and policy-making requires identifying possible stakeholders and actors and their characteristics, in the current study a stakeholder analysis and also a social network analysis (SNA) was conducted to identify the potential stakeholders and also their characteristics involved in physical rehabilitation financing (PRF)-related policies in Iran.

METHODS

The present study was performed in two phases. Firstly, semi-structured interviews and relevant document review were conducted to identify the stakeholders. Then, the position, power, interest, and influence of each stakeholder were determined using a web-based questionnaire. Secondly, SNA approach was utilized to map and visualize the interactions among stakeholders.

RESULTS

The findings showed that there are different stakeholders in PRF-related decision- and policy-making processes in Iran. In addition, the position, power, interest, and influence level of the identified stakeholders were varied. Moreover, although some stakeholders, like the Ministry of Health and the parliament have the highest level of power and position, they lack sufficient interest to participate in PRF-policies. Furthermore, SNA demonstrated that social network density was low, which indicates the lack of proper collaboration and interaction among the stakeholders.

CONCLUSION

As many powerful and influential stakeholders had low interest levels to warrant participate in the FPR-related decision- and policy-making processes in Iran, employing careful and effective strategies, that is ongoing negotiations, receiving advocacy, and making senior managers and policy-makers aware can be helpful.

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.

Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution

Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.

Family relationships and adolescent loneliness: An application of social network analysis in family studies

In family contexts, individuals are embedded in networks of relationships. Social network analysis provides a unique framework to investigate family relationships as interrelated networks above and beyond dyadic familial relationships. In the current paper, we used the notion of triadic closure to investigate how various configurations of family networks, classified by their relationship ties, differ in predicting adolescents' experiences of loneliness. We classified different types of network structures based on whether all 3 family members (i.e., child, mother, father) shared high-quality relationships with one another (closed) or whether 1 or more low quality ties existed in the family triad (open). Results indicated that, compared with adolescents in families containing 1 or more poor-quality ties, adolescents in families containing all high-quality relational ties experienced lower levels of loneliness, above and beyond the impact of gender, parents' education and mental health, and family income. Simply put, adolescents' experiences of loneliness is not tied to the number of high-quality relationships they experience within the family but rather is dependent on the presence of high-quality relationships among all family ties. With the introduction of 1 low-quality relationship within a family triad, additional low-quality relationships appear to make little difference. In line with family systems theory, our examination of the family as a whole, rather than as a summative combination of smaller relationships, indicates that a closed family structure is important for protecting adolescents against experiences of loneliness. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Distinguishing Characteristics of Corruption Risks in Iranian Construction Projects: A Weighted Correlation Network Analysis

The construction industry consistently ranks amongst the highest contributors to global gross domestic product, as well as, amongst the most corrupt. Corruption therefore inflicts significant risk on construction activities, and overall economic development. These facts are widely known, but the various sources and nature of corruption risks endemic to the Iranian construction industry, along with the degree to which such risks manifest, and the strength of their impact, remain undescribed. To address the gap, a mixed methods approach is used; with a questionnaire, 103 responses were received, and these were followed up with semi-structured interviews. Results were processed using social network analysis. Four major corruption risks were identified: (1) procedural violations in awarding contracts, (2) misuse of contractual arrangements, (3) neglect of project management principles, and, (4) irrational decision making. While corruption risks in Iran align with those found in other countries, with funds being misappropriated for financial gain, Iran also shows a strong inclination to champion projects that serve the government's political agenda. Root cause identification of corruption risks, namely, the noticeable impact of authoritarianism on project selection in Iran, over criterion of economic benefit or social good, is a significant outcome of this study.

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.

Importance of scientific collaboration in contemporary drug discovery and development: a detailed network analysis

BACKGROUND

Growing evidence shows that scientific collaboration plays a crucial role in transformative innovation in the life sciences. For example, contemporary drug discovery and development reflects the work of teams of individuals from academic centers, the pharmaceutical industry, the regulatory science community, health care providers, and patients. However, public understanding of how collaborations between academia and industry catalyze novel target identification and first-in-class drug discovery is limited.

RESULTS

We perform a comprehensive network analysis on a large scientific corpus of collaboration and citations (97,688 papers with 1,862,500 citations from 170 million scientific records) to quantify the success trajectory of innovative drug development. By focusing on four types of cardiovascular drugs, we demonstrate how knowledge flows between institutions to highlight the underlying contributions of many different institutions in the development of a new drug. We highlight how such network analysis could help to increase industrial and governmental support, and improve the efficiency or accelerate decision-making in drug discovery and development.

CONCLUSION

We demonstrate that network analysis of large public databases can identify and quantify investigator and institutional relationships in drug discovery and development. If broadly applied, this type of network analysis may help to enhance public understanding of and support for biomedical research, and could identify factors that facilitate decision-making in first-in-class drug discovery among academia, the pharmaceutical industry, and healthcare systems.