scholarly journals Incorporating Geographical Contacts into Social Network Analysis for Contact Tracing in Epidemiology: A Study on Taiwan SARS Data

2007 ◽  
pp. 23-36 ◽  
Author(s):  
Yi-Da Chen ◽  
Chunju Tseng ◽  
Chwan-Chuen King ◽  
Tsung-Shu Joseph Wu ◽  
Hsinchun Chen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Contact Tracing

scholarly journals Social network analysis methods for exploring SARS-CoV-2 contact tracing data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Karikalan Nagarajan ◽  
Malaisamy Muniyandi ◽  
Bharathidasan Palani ◽  
Senthil Sellappan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Disease Transmission ◽  
Contact Tracing ◽  
Centrality Measures ◽  
Degree Centrality ◽  
Individual Patient Level ◽  
Patient Level ◽  
Network Measures

Abstract Background Contact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels. Methods Standard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as “nodes” and their epidemiological contact as “edges” was created. Directed network perspective was utilized in which directionality of infection emanated from a “source patient” towards a “target patient”. Network measures of “ degree centrality” and “betweenness centrality” were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers. Results Out-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (> = 10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient’s which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network. Conclusions Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.

scholarly journals A Social Network Analysis Approach for Contact Tracing in the Hospital Setting

2020 ◽  
Vol 6 (3) ◽  
pp. 22-25
Author(s):  
Mina Ostovari ◽  
Claudine Jurkovitz ◽  
Lee Pachter ◽  
David Chen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Hospital Setting ◽  
Contact Tracing ◽  
Analysis Approach

scholarly journals Epidemics and Networks: A Social Network Analysis of the Spread of COVID-19 in South Korea and Policy Implications

2020 ◽  
Author(s):  
Wonkwang Jo ◽  
Dukjin Chang ◽  
Myoungsoon You ◽  
Ghi-Hoon Ghim
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Metropolitan Areas ◽  
Structural Characteristics ◽  
Skewed Distribution ◽  
Policy Implications ◽  
Contact Tracing ◽  
Network Data ◽  
Network Change

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 3,283 confirmed patients in Seoul metropolitan areas from Jan 20 to July 19, 2020, this study creates an infection network and analyzes its structural characteristics. The main results are as follows: (1) out-degrees follow an extremely positively skewed distribution, and (2) removing the top nodes on the out-degree significantly decreases the size of the infection network. (3) The indicators, which 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.

scholarly journals Social network analysis of COVID-19 transmission in Karnataka, India

2020 ◽  
Vol 148 ◽  
Author(s):  
S. Saraswathi ◽  
A. Mukhopadhyay ◽  
H. Shah ◽  
T. S. Ranganath
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Cluster Formation ◽  
Contact Tracing ◽  
The Novel ◽  
Monitoring And Control ◽  
Healthcare Administrators ◽  
Novel Coronavirus ◽  
And Control

Abstract We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11–40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1–47 for 199 (17.35%) nodes, and betweenness, 0.5–87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) ‘super-spreaders’ (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

scholarly journals Social Network Analysis of COVID-19 Transmission in Karnataka, India

2020 ◽  
Author(s):  
Sakranaik Saraswathi ◽  
Amita Mukhopadhyay ◽  
Hemant Shah ◽  
T S Ranganath
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Cluster Formation ◽  
Contact Tracing ◽  
Monitoring And Control ◽  
The Government ◽  
Healthcare Administrators ◽  
Novel Coronavirus ◽  
And Control

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and assess the potential of SNA as a tool for outbreak monitoring and control. We analyzed contact tracing data of 1147 Covid-19 positive cases (mean age 34.91 years, 61.99% aged 11−40, 742 males), anonymized and made public by the government. We used software tools Cytoscape and Gephi to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links, determined by contact tracing, from source to target patients). Outdegree was 1−47 for 199 (17.35%) nodes, and betweenness 0.5−87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) super-spreaders (outdegree≥5) caused 60% of the transmissions. Real-time social network visualization can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritizing these areas and individuals for rigorous containment could help minimize resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wonkwang Jo ◽  
Dukjin Chang ◽  
Myoungsoon You ◽  
Ghi-Hoon Ghim
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Metropolitan Areas ◽  
Structural Characteristics ◽  
Skewed Distribution ◽  
Policy Implications ◽  
Contact Tracing ◽  
Network Data ◽  
Network Change

AbstractThis 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.

Sign in / Sign up

Export Citation Format

Share Document