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1.
PLoS One ; 16(12): e0261335, 2021.
Article in English | MEDLINE | ID: covidwho-1571992

ABSTRACT

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.


Subject(s)
COVID-19/transmission , Contact Tracing/methods , Adolescent , Adult , Aged , Algorithms , Child , Child, Preschool , China/epidemiology , Female , Humans , Infant , Male , Middle Aged , Social Network Analysis , Tourism , Young Adult
2.
Epidemiol Health ; 43: e2021068, 2021.
Article in English | MEDLINE | ID: covidwho-1512896

ABSTRACT

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.


Subject(s)
COVID-19 , Social Network Analysis , Disease Outbreaks , Humans , Republic of Korea/epidemiology , SARS-CoV-2
3.
Sci Rep ; 11(1): 22055, 2021 11 11.
Article in English | MEDLINE | ID: covidwho-1510617

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , Social Network Analysis , Social Networking , Female , Housing , Humans , Male , Pandemics , Public Health , SARS-CoV-2/isolation & purification , Students , Universities
4.
Math Biosci ; 338: 108645, 2021 08.
Article in English | MEDLINE | ID: covidwho-1492387

ABSTRACT

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%.


Subject(s)
COVID-19/prevention & control , Contact Tracing/statistics & numerical data , Disease Notification/standards , Models, Theoretical , Social Network Analysis , Adult , Computer Simulation , Humans , Medical Informatics Applications , Mobile Applications , Quarantine/statistics & numerical data , Students , Young Adult
5.
Sci Rep ; 11(1): 19655, 2021 10 04.
Article in English | MEDLINE | ID: covidwho-1450294

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , Social Isolation , Social Media , COVID-19/pathology , COVID-19/virology , Humans , Pandemics , Politics , SARS-CoV-2/isolation & purification , Social Network Analysis , Social Networking
6.
J Korean Acad Nurs ; 51(4): 442-453, 2021 Aug.
Article in Korean | MEDLINE | ID: covidwho-1403931

ABSTRACT

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.


Subject(s)
COVID-19 , Newspapers as Topic/statistics & numerical data , Nurses/psychology , Social Network Analysis , Humans , Pandemics , SARS-CoV-2
7.
CMAJ ; 193(31): E1203-E1212, 2021 08 09.
Article in English | MEDLINE | ID: covidwho-1350176

ABSTRACT

BACKGROUND: The COVID-19 pandemic has exacerbated disparities in poverty and illness for people in vulnerable circumstances in ethnocultural communities. We sought to understand the evolving impacts of COVID-19 on ethnocultural communities to inform intersectoral advocacy and community action. METHODS: The Illuminate Project used participatory action research, with cultural health brokers as peer researchers, from Sept. 21 to Dec. 31, 2020, in Edmonton, Alberta. Twenty-one peer researchers collected narratives from members of ethnocultural communities and self-interpreted them as they entered the narratives into the SenseMaker platform, a mixed-method data collection tool. The entire research team analyzed real-time, aggregate, quantitative and qualitative data to identify emerging thematic domains, then visualized these domains with social network analysis. RESULTS: Brokers serving diverse communities collected 773 narratives. Identified domains illuminate the evolving and entangled impacts of COVID-19 including the following: COVID-19 prevention and management; care of acute, chronic and serious illnesses other than COVID-19; maternal care; mental health and triggers of past trauma; financial insecurity; impact on children and youth and seniors; and legal concerns. We identified that community social capital and cultural brokering are key assets that facilitate access to formal health and social system supports. INTERPRETATION: The Illuminate Project has illustrated the entangled, systemic issues that result in poor health among vulnerable members of ethnocultural communities, and the exacerbating effects of COVID-19, which also increased barriers to mitigation. Cultural brokering and community social capital are key supports for people during the COVID-19 pandemic. These findings can inform policy to reduce harm and support community resiliency.


Subject(s)
COVID-19/ethnology , Community Health Services/organization & administration , Pandemics , Vulnerable Populations/ethnology , Alberta/epidemiology , COVID-19/prevention & control , COVID-19/therapy , Consumer Health Information , Female , Financial Stress , Health Services Research , Healthcare Disparities , Humans , Male , Poverty , SARS-CoV-2 , Social Capital , Social Network Analysis , Social Support
8.
Sci Rep ; 11(1): 14877, 2021 07 21.
Article in English | MEDLINE | ID: covidwho-1320239

ABSTRACT

The COVID-19 pandemic has meant that young university students have had to adapt their learning and have a reduced relational context. Adversity contexts build models of human behaviour based on relationships. However, there is a lack of studies that analyse the behaviour of university students based on their social structure in the context of a pandemic. This information could be useful in making decisions on how to plan collective responses to adversities. The Social Network Analysis (SNA) method has been chosen to address this structural perspective. The aim of our research is to describe the structural behaviour of students in university residences during the COVID-19 pandemic with a more in-depth analysis of student leaders. A descriptive cross-sectional study was carried out at one Spanish Public University, León, from 23th October 2020 to 20th November 2020. The participation was of 93 students, from four halls of residence. The data were collected from a database created specifically at the university to "track" contacts in the COVID-19 pandemic, SiVeUle. We applied the SNA for the analysis of the data. The leadership on the university residence was measured using centrality measures. The top leaders were analyzed using the Egonetwork and an assessment of the key players. Students with higher social reputations experience higher levels of pandemic contagion in relation to COVID-19 infection. The results were statistically significant between the centrality in the network and the results of the COVID-19 infection. The most leading students showed a high degree of Betweenness, and three students had the key player structure in the network. Networking behaviour of university students in halls of residence could be related to contagion in the COVID-19 pandemic. This could be described on the basis of aspects of similarities between students, and even leaders connecting the cohabitation sub-networks. In this context, Social Network Analysis could be considered as a methodological approach for future network studies in health emergency contexts.


Subject(s)
COVID-19/psychology , Social Interaction , Social Networking , Students/psychology , COVID-19/epidemiology , Cross-Sectional Studies , Female , Housing , Humans , Learning , Male , Pandemics , SARS-CoV-2/isolation & purification , Social Behavior , Social Isolation/psychology , Social Network Analysis , Spain/epidemiology , Surveys and Questionnaires , Universities , Young Adult
9.
Math Biosci ; 339: 108648, 2021 09.
Article in English | MEDLINE | ID: covidwho-1294054

ABSTRACT

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.


Subject(s)
COVID-19 , Communicable Disease Control , Models, Statistical , Social Network Analysis , Spatial Analysis , Age Factors , COVID-19/prevention & control , COVID-19/transmission , Communicable Disease Control/methods , Communicable Disease Control/standards , Communicable Disease Control/statistics & numerical data , Germany , Humans
10.
Int J Environ Res Public Health ; 18(12)2021 06 08.
Article in English | MEDLINE | ID: covidwho-1282457

ABSTRACT

Using social media is one important strategy to communicate research and public health guidelines to the scientific community and general public. Empirical evidence about which communication strategies are effective around breastfeeding messaging is scarce. To fill this gap, we aimed to identify influencers in the largest available Twitter database using social network analysis (n = 10,694 users), inductively analyze tweets, and explore communication strategies, motivations, and challenges via semi-structured interviews. Influencers had diverse backgrounds within and beyond the scientific health community (SHC; 42.7%): 54.7% were from the general public and 3% were companies. SHC contributed to most of the tweets (n = 798 tweets), disseminating guidelines and research findings more frequently than others (p < 0.001). Influencers from the general community mostly tweeted opinions regarding the current state of breastfeeding research and advocacy. Interviewees provided practical strategies (e.g., preferred visuals, tone, and writing style) to achieve personal and societal goals including career opportunities, community support, and improved breastfeeding practices. Complex challenges that need to be addressed were identified. Ideological differences regarding infant feeding may be hampering constructive communication, including differences in influencers' interpretation of the WHO International Code of Marketing of Breast-milk Substitutes and in perspectives regarding which social media interactions encompass conflict of interest.


Subject(s)
Nursing Research , Social Media , Breast Feeding , Communication , Female , Humans , Social Network Analysis
11.
Sci Rep ; 11(1): 8581, 2021 04 21.
Article in English | MEDLINE | ID: covidwho-1196849

ABSTRACT

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.


Subject(s)
COVID-19/transmission , Contact Tracing/methods , Social Network Analysis , Health Policy , Humans , Republic of Korea , SARS-CoV-2/isolation & purification
12.
Inform Health Soc Care ; 46(4): 443-454, 2021 Dec 02.
Article in English | MEDLINE | ID: covidwho-1193686

ABSTRACT

Burden due to infectious and noncommunicable disease is increasing at an alarming rate. Social media usage is growing rapidly and has become the new norm of communication. It is imperative to examine what is being discussed in the social media about diseases or conditions and the characteristics of the network of people involved in discussion. The objective is to assess the tools and techniques used to study social media disease networks using network analysis and network modeling. PubMed and IEEEXplore were searched from 2009 to 2020 and included 30 studies after screening and analysis. Twitter, QuitNet, and disease-specific online forums were widely used to study communications on various health conditions. Most of the studies have performed content analysis and network analysis, whereas network modeling has been done in six studies. Posts on cancer, COVID-19, and smoking have been widely studied. Tools and techniques used for network analysis are listed. Health-related social media data can be leveraged for network analysis. Network modeling technique would help to identify the structural factors associated with the affiliation of the disease networks, which is scarcely utilized. This will help public health professionals to tailor targeted interventions.


Subject(s)
COVID-19 , Social Media , Humans , Public Health , SARS-CoV-2 , Social Network Analysis
13.
J Med Internet Res ; 23(3): e27009, 2021 03 25.
Article in English | MEDLINE | ID: covidwho-1150653

ABSTRACT

BACKGROUND: The dissemination of rumor rebuttal content on social media is vital for rumor control and disease containment during public health crises. Previous research on the effectiveness of rumor rebuttal, to a certain extent, ignored or simplified the structure of dissemination networks and users' cognition as well as decision-making and interaction behaviors. OBJECTIVE: This study aimed to roughly evaluate the effectiveness of rumor rebuttal; dig deeply into the attitude-based echo chamber effect on users' responses to rumor rebuttal under multiple topics on Weibo, a Chinese social media platform, in the early stage of the COVID-19 epidemic; and evaluate the echo chamber's impact on the information characteristics of user interaction content. METHODS: We used Sina Weibo's application programming interface to crawl rumor rebuttal content related to COVID-19 from 10 AM on January 23, 2020, to midnight on April 8, 2020. Using content analysis, sentiment analysis, social network analysis, and statistical analysis, we first analyzed whether and to what extent there was an echo chamber effect on the shaping of individuals' attitudes when retweeting or commenting on others' tweets. Then, we tested the heterogeneity of attitude distribution within communities and the homophily of interactions between communities. Based on the results at user and community levels, we made comprehensive judgments. Finally, we examined users' interaction content from three dimensions-sentiment expression, information seeking and sharing, and civility-to test the impact of the echo chamber effect. RESULTS: Our results indicated that the retweeting mechanism played an essential role in promoting polarization, and the commenting mechanism played a role in consensus building. Our results showed that there might not be a significant echo chamber effect on community interactions and verified that, compared to like-minded interactions, cross-cutting interactions contained significantly more negative sentiment, information seeking and sharing, and incivility. We found that online users' information-seeking behavior was accompanied by incivility, and information-sharing behavior was accompanied by more negative sentiment, which was often accompanied by incivility. CONCLUSIONS: Our findings revealed the existence and degree of an echo chamber effect from multiple dimensions, such as topic, interaction mechanism, and interaction level, and its impact on interaction content. Based on these findings, we provide several suggestions for preventing or alleviating group polarization to achieve better rumor rebuttal.


Subject(s)
COVID-19/epidemiology , Social Media/statistics & numerical data , Social Network Analysis , COVID-19/psychology , China/epidemiology , Humans , SARS-CoV-2/isolation & purification
14.
Int J Environ Res Public Health ; 18(5)2021 03 03.
Article in English | MEDLINE | ID: covidwho-1125641

ABSTRACT

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.


Subject(s)
COVID-19 , Pandemics , China/epidemiology , Europe , Far East , Humans , SARS-CoV-2 , Social Network Analysis , United States
15.
Proc Natl Acad Sci U S A ; 118(1)2021 01 07.
Article in English | MEDLINE | ID: covidwho-1066037

ABSTRACT

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.


Subject(s)
COVID-19/epidemiology , Nursing Homes , Pandemics , SARS-CoV-2/pathogenicity , COVID-19/prevention & control , COVID-19/virology , Disease Outbreaks , Female , Humans , Male , Skilled Nursing Facilities , Smartphone , Social Network Analysis , Social Networking
16.
PLoS One ; 16(1): e0242955, 2021.
Article in English | MEDLINE | ID: covidwho-1044112

ABSTRACT

Human behavior (movement, social contacts) plays a central role in the spread of pathogens like SARS-CoV-2. The rapid spread of SARS-CoV-2 was driven by global human movement, and initial lockdown measures aimed to localize movement and contact in order to slow spread. Thus, movement and contact patterns need to be explicitly considered when making reopening decisions, especially regarding return to work. Here, as a case study, we consider the initial stages of resuming research at a large research university, using approaches from movement ecology and contact network epidemiology. First, we develop a dynamical pathogen model describing movement between home and work; we show that limiting social contact, via reduced people or reduced time in the workplace are fairly equivalent strategies to slow pathogen spread. Second, we develop a model based on spatial contact patterns within a specific office and lab building on campus; we show that restricting on-campus activities to labs (rather than labs and offices) could dramatically alter (modularize) contact network structure and thus, potentially reduce pathogen spread by providing a workplace mechanism to reduce contact. Here we argue that explicitly accounting for human movement and contact behavior in the workplace can provide additional strategies to slow pathogen spread that can be used in conjunction with ongoing public health efforts.


Subject(s)
COVID-19/transmission , Contact Tracing , Return to Work , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control , Computer Simulation , Humans , Models, Biological , Movement , Social Interaction , Social Network Analysis , Transportation , Workplace
17.
Addict Behav ; 114: 106754, 2021 03.
Article in English | MEDLINE | ID: covidwho-956847

ABSTRACT

Research shows that there has been a substantial increase in substance use and abuse during the COVID-19 pandemic, and that substance use/abuse is a commonly reported way of coping with anxiety concerning COVID-19. Anxiety about COVID-19 is more than simply worry about infection. Research provides evidence of a COVID Stress Syndrome characterized by (1) worry about the dangers of COVID-19 and worry about coming into contact with coronavirus contaminated objects or surfaces, (2) worry about the personal socioeconomic impact of COVID-19, (3) xenophobic worries that foreigners are spreading COVID-19, (4) COVID-19-related traumatic stress symptoms (e.g., nightmares), and (5) COVID-19-related compulsive checking and reassurance-seeking. These form a network of interrelated nodes. Research also provides evidence of another constellation or "syndrome", characterized by (1) belief that one has robust physical health against COVID-19, (2) belief that the threat of COVID-19 has been exaggerated, and (3) disregard for social distancing. These also form a network of nodes known as a COVID-19 Disregard Syndrome. The present study, based on a population-representative sample of 3075 American and Canadian adults, sought to investigate how these syndromes are related to substance use and abuse. We found substantial COVID-19-related increases in alcohol and drug use. Network analyses indicated that although the two syndromes are negatively correlated with one another, they both have positive links to alcohol and drug abuse. More specifically, COVID-19-related traumatic stress symptoms and the tendency to disregard social distancing were both linked to substance abuse. Clinical and public health implications are discussed.


Subject(s)
COVID-19/psychology , Physical Distancing , Quarantine/psychology , Social Network Analysis , Stress, Psychological/psychology , Substance-Related Disorders/psychology , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Pandemics , Young Adult
18.
J Travel Med ; 28(2)2021 02 23.
Article in English | MEDLINE | ID: covidwho-745783

ABSTRACT

BACKGROUND: Low-wage dormitory-dwelling migrant workers in Singapore were disproportionately affected by coronavirus disease 2019 (COVID-19) infection. This was attributed to communal living in high-density and unhygienic dormitory settings and a lack of inclusive protection systems. However, little is known about the roles of social and geospatial networks in COVID-19 transmission. The study examined the networks of non-work-related activities among migrant workers to inform the development of lockdown exit strategies and future pandemic preparedness. METHODS: A population-based survey was conducted with 509 migrant workers across the nation, and it assessed dormitory attributes, social ties, physical and mental health status, COVID-19-related variables and mobility patterns using a grid-based network questionnaire. Mobility paths from dormitories were presented based on purposes of visit. Two-mode social networks examined the structures and positions of networks between workers and visit areas with individual attributes. RESULTS: COVID-19 risk exposure was associated with the density of dormitory, social ties and visit areas. The migrant worker hub in the city centre was the most frequently visited for essential services of grocery shopping and remittance, followed by south central areas mainly for social gathering. The hub was positioned as the core with the highest degree of centrality with a cluster of workers exposed to COVID-19. CONCLUSIONS: Social and geospatial networks of migrant workers should be considered in the implementation of lockdown exit strategies while addressing the improvement of living conditions and monitoring systems. Essential services, like remittance and grocery shopping at affordable prices, need to be provided near to dormitories to minimize excess gatherings.


Subject(s)
COVID-19/epidemiology , Health Equity/standards , Transients and Migrants/statistics & numerical data , Adult , Built Environment/standards , COVID-19/transmission , Female , Humans , Male , Pandemics , Population Density , Prevalence , Risk Assessment , SARS-CoV-2 , Singapore/epidemiology , Social Network Analysis , Spatial Analysis , Surveys and Questionnaires , Young Adult
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