Whole-genome sequencing and social-network analysis of a tuberculosis outbreak.

By Jennifer L. Gardy, J. Johnston, Shannan J. Ho Sui, V. Cook, L. Shah, E. Brodkin, S. Rempel, Richard A. Moore, Yongjun Zhao, Robert W. Holt and 9 more. New England Journal of Medicine, 2011

749 citations recorded by the source corpus.

Subfields: Epidemiology, Genetics, Infectious Diseases

View Paper: https://www.semanticscholar.org/paper/7777290f76deafabe85612faa1c17be1480efcb4

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1. Javier Andreu-Perez
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3. Henry M. D. Ip
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Citations in the corpus

• Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges (2016, neutral): “For example, social network systems have been used to examine friendship groups (Eagle, Pentland, & Lazer, 2009), online social media interactions (Brown, Broderick, & Lee, 2007), and disease transmission (Gardy et  al., 2011).”
• IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry (2020, neutral): “The combination of socio-environmental information and whole-genome sequencing of prevailing and historical isolates were used by Gardy, et al. [110] to ascertain the point of origin of a tuberculosis outbreak.”
• Official American Thoracic Society/Infectious Diseases Society of America/Centers for Disease Control and Prevention Clinical Practice Guidelines: Diagnosis of Tuberculosis in Adults and Children (2017, neutral): “to investigation of tuberculosis outbreaks [240].”
• From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare (2015, methodology): “It ha s also been used in health crises and epidemic studies such as in the case of severe acute respiratory syndrome, H1N1 influenz a, tuberculosis outbreaks [80, 81], and more recently muted to track Ebola [82].”