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The Role of Location in the Spread of SARS-CoV-2: Examination of Cases and Exposed Contacts in South Texas, Using Social Network Analysis

Published online by Cambridge University Press:  23 October 2023

Eric C. Jones ,
Daniella Rodriguez ,
David Gimeno Ruiz de Porras ,
Anita Kurian  and
Jack Tsai Open the ORCID record for Jack Tsai [Opens in a new window]
Eric C. Jones
Affiliation:
The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
Daniella Rodriguez
Affiliation:
The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
David Gimeno Ruiz de Porras
Affiliation:
Southwest Center for Occupational and Environmental Health Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA Center for Research in Occupational Health (CiSAL), Universitat Pompeu Fabra, Barcelona, Spain CIBER of Epidemiology and Public Health, Madrid, Spain
Anita Kurian
Affiliation:
San Antonio Metropolitan Health District, San Antonio, TX, USA
Jack Tsai*
Affiliation:
The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
*
Corresponding author: Jack Tsai, Email: [email protected]
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Abstract

Objective:

This study sought to better understand the types of locations that serve as hubs for the transmission of COVID-19.

Methods:

Contact tracers interviewed individuals who tested positive for SARS-CoV-2 between November 2020 and March 2021, as well as the people with whom those individuals had contact. We conducted a 2-mode social network analysis of people by the types of places they visited, focusing on the forms of centrality exhibited by place types.

Results:

The most exposed locations were grocery stores, commercial stores, restaurants, commercial services, and schools. These types of locations also have the highest “betweenness,” meaning that they tend to serve as hubs between other kinds of locations since people would usually visit more than 1 location in a day or when infected. The highest pairs of locations were grocery store/retail store, restaurant/retail store, and restaurant/grocery store. Schools are not at the top but are 3 times in the top 7 pairs of locations and connected to the 3 types of locations in those top pairs.

Conclusions:

As the pandemic progressed, location hotspots shifted between businesses, schools, and homes. In this social network analysis, certain types of locations appeared to be potential hubs of transmission.

Keywords

2-mode networksCOVIDdisease spreadhubsplaces
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e516
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health

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References

Spencer, KD, Chung, CL, Stargel, A, et al. COVID-19 case investigation and contact tracing efforts from health departments—United States, June 25–July 24, 2020. Morb Mortal Wkly Rep. 2020;70(3):83-87. http://doi.org/10.15585/mmwr.mm7003a3 CrossRefGoogle Scholar
Kırbıyık, U, Binder, AM, Ghinai, I, et al. Network characteristics and visualization of COVID-19 outbreak in a large detention facility in the United States—Cook County, Illinois, 2020. Morb Mortal Wkly Rep. 2020;69:1625-1630. http://doi.org/10.15585/mmwr.mm6944a3 CrossRefGoogle Scholar
Wang, P, Lu, JA, Jin, Y, et al. Statistical and network analysis of 1212 COVID-19 patients in Henan, China. Int J Infect Dis. 2020;95:391-398. https://doi.org/10.1016/j.ijid.2020.04.051 CrossRefGoogle ScholarPubMed
Nagarajan, K, Muniyandi, M, Palani, B, et al. Social network analysis methods for exploring SARS-CoV-2 contact tracing data. BMC Med Res Methodol. 2020;20:233. https://doi.org/10.1186/s12874-020-01119-3 CrossRefGoogle ScholarPubMed
Ending Isolations and Precautions for People with COVID-19: Interim Guidance. CDC. Published August 22, 2023. Accessed August 25, 2023. https://www.cdc.gov/coronavirus/2019-ncov/hcp/duration-isolation.html Google Scholar
Wasserman, S, Faust, K. Social network analysis: methods and applications. Cambridge University Press; 1994.CrossRefGoogle Scholar
Chang, S, Pierson, E, Koh, PW, et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2021;589:82-87. https://doi.org/10.1038/s41586-020-2923-3 CrossRefGoogle ScholarPubMed