Dataset Information

Network canvas: an open-source tool for capturing social and contact network data.

ABSTRACT:

Motivation

Social influence and contact networks are extremely important for understanding health behaviour and the spread of disease. Yet, most traditional software tools are not optimized to capture these data, making measurement of personal networks challenging. Our team developed Network Canvas to provide an end-to-end workflow with intuitive interfaces to enable researchers to design and conduct network interviews.

Implementation

Network Canvas consists of three applications (Architect, Interviewer and Server). All applications are written in JavaScript and run on Windows, macOS and Linux; Interviewer also runs on Android and iOS.

General features

Network Canvas substantially reduces the complexity and technical knowledge required to collect network data via three point-and-click applications. The tool has wide applicability for measuring contact and social influence networks in epidemiology.

Availability

Network Canvas is open source and freely available [networkcanvas.com] under the GNU General Public License 3.

SUBMITTER: Janulis P

PROVIDER: S-EPMC10396415 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Publications

Network canvas: an open-source tool for capturing social and contact network data.

Janulis Patrick P Phillips Gregory G Melville Joshua J Hogan Bernie B Banner Kate K Mustanski Brian B Oser Carrie B CB Tillson Martha M Schneider John J Birkett Michelle M

International journal of epidemiology 20230801 4

<h4>Motivation</h4>Social influence and contact networks are extremely important for understanding health behaviour and the spread of disease. Yet, most traditional software tools are not optimized to capture these data, making measurement of personal networks challenging. Our team developed Network Canvas to provide an end-to-end workflow with intuitive interfaces to enable researchers to design and conduct network interviews.<h4>Implementation</h4>Network Canvas consists of three applications ...[more]

PMID: 36944105

Similar Datasets

Project description: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.

Dataset Information

Network canvas: an open-source tool for capturing social and contact network data.

Motivation

Implementation

General features

Availability

Publications

Network canvas: an open-source tool for capturing social and contact network data.

Similar Datasets

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