{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:29:28Z","timestamp":1753892968312,"version":"3.41.2"},"reference-count":12,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>COVID-19 has profoundly impacted global daily life, emphasizing the need for effective virus suppression strategies. In response, China has established numerous nucleic acid testing sites to facilitate rapid testing and curb outbreaks. However, these sites often experience congestion, increasing transmission risks and reducing testing efficiency. This study focuses on the spatial\u2013temporal analysis of testing site distribution and associated infection risks in Shenzhen, China. Data from all Shenzhen testing sites were analyzed for the week of October 24\u201330, 2022, noting the percentage of busy hours per site and incorporating a population size factor by district to assess regional infection risks. Findings indicate three daily peak testing times\u2014primarily in the evening\u2014with the highest risk of transmission in Longgang District, followed by Yantian and Luohu, and the lowest in Futian. The risk coefficient varied from 0.040 to 0.349, with most areas showing stable risk levels between 0.06 and 0.20. This research underlines the necessity for policymakers to alleviate congestion at testing sites and suggests increasing site availability in Longgang District to mitigate COVID-19 spread, offering methodological guidance for managing infection risks in other major Chinese cities.<\/jats:p>","DOI":"10.3389\/fpubh.2024.1420497","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T16:41:54Z","timestamp":1729528914000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Infection risk factor measurement based on spatial and temporal distribution of COVID-19 nucleic acid test sites: an example from Shenzhen"],"prefix":"10.3389","volume":"12","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longhao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianlin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","article-title":"Clinical characteristics of coronavirus disease 2019 in China","volume":"382","author":"Guan","year":"2020","journal-title":"N Engl J Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"2603","DOI":"10.1056\/NEJMoa2034577","article-title":"Safety and efficacy of the BNT162b2 mRNA COVID-19 vaccine","volume":"383","author":"Polack","year":"2020","journal-title":"N Engl J Med"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/s41582-021-00603-8","article-title":"Safety and efficacy of COVID-19 vaccines in people with neurological disorders","volume":"18","author":"Wood","year":"2022","journal-title":"Nat Rev Neurol"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"100495","DOI":"10.1016\/j.hjdsi.2020.100495","article-title":"Association of race, ethnicity, and community-level factors with COVID-19 cases and deaths across U.S. counties","volume":"9","author":"Figueroa","year":"2021","journal-title":"Healthcare"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"103144","DOI":"10.1016\/j.scs.2021.103144","article-title":"The impacts of the built environment on the incidence rate of COVID-19: a case study of King County, Washington","volume":"74","author":"Liu","year":"2021","journal-title":"Sustain Cities Soc"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"103396","DOI":"10.1016\/j.cities.2021.103396","article-title":"What county-level factors influence COVID-19 incidence in the United States? 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