ArticleJournal of the American Medical Informatics Association : JAMIA2021
Mining relationships between transmission clusters from contact tracing data: An application for investigating COVID-19 outbreak.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Variability in the Population Diffusion Patterns of SARS-CoV-2 by Exposure Setting and Its Roles in Driving Epidemic Dynamics.Influenza and other respiratory viruses · 2025Article
- Mass Screening of SARS-CoV-2 With Rapid Antigen Tests in a Receding Omicron Wave: Population-Based Survey for Epidemiologic Evaluation.JMIR public health and surveillance · 2022Article
- Enforcement of the Use of Digital Contact-Tracing Apps in a Common Law Jurisdiction.Healthcare (Basel, Switzerland) · 2022Article
- Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveContact tracing of reported infections could enable close contacts to be identified, tested, and quarantined for controlling further spread. This strategy has been well demonstrated in the surveillance and control of COVID-19 (coronavirus disease 2019) epidemics. This study aims to leverage contact tracing data to investigate the degree of spread and the formation of transmission cascades composing of multiple clusters. MATERIALS AND
methodsAn algorithm on mining relationships between clusters for network analysis is proposed with 3 steps: horizontal edge creation, vertical edge consolidation, and graph reduction. The constructed network was then analyzed with information diffusion metrics and exponential-family random graph modeling. With categorization of clusters by exposure setting, the metrics were compared among cascades to identify associations between exposure settings and their network positions within the cascade using Mann-Whitney U test.
resultsExperimental results illustrated that transmission cascades containing or seeded by daily activity clusters spread faster while those containing social activity clusters propagated farther. Cascades involving work or study environments consisted of more clusters, which had a higher transmission range and scale. Social activity clusters were more likely to be connected, whereas both residence and healthcare clusters did not preferentially link to clusters belonging to the same exposure setting.
conclusionsThe proposed algorithm could contribute to in-depth epidemiologic investigation of infectious disease transmission to support targeted nonpharmaceutical intervention policies for COVID-19 epidemic control.
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