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Prioritizing COVID-19 Contact Tracing During a Surge Using Chatbot Technology.
Johnson, Brady D; Shui, Meg Wall; Said, Kiana; Chavez, Alejandro; Sachdev, Darpun D.
  • Johnson BD; All authors are with the Population Health Division, San Francisco Department of Public Health, San Francisco, CA. Brady D. Johnson is also with the Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
  • Shui MW; All authors are with the Population Health Division, San Francisco Department of Public Health, San Francisco, CA. Brady D. Johnson is also with the Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
  • Said K; All authors are with the Population Health Division, San Francisco Department of Public Health, San Francisco, CA. Brady D. Johnson is also with the Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
  • Chavez A; All authors are with the Population Health Division, San Francisco Department of Public Health, San Francisco, CA. Brady D. Johnson is also with the Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
  • Sachdev DD; All authors are with the Population Health Division, San Francisco Department of Public Health, San Francisco, CA. Brady D. Johnson is also with the Department of Tropical Medicine, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
Am J Public Health ; 112(1): 43-47, 2022 01.
Article in English | MEDLINE | ID: covidwho-1594512
ABSTRACT
When COVID-19 cases surge, identifying ways to improve the efficiency of contact tracing and prioritize vulnerable communities for isolation and quarantine support services is critical. During a fall 2020 COVID-19 resurgence in San Francisco, California, prioritization of telephone-based case investigation by zip code and using a chatbot to screen for case participants who needed isolation support reduced the number of case participants who would have been assigned for a telephone interview by 31.5% and likely contributed to 87.5% of Latinx case participants being successfully interviewed. (Am J Public Health. 2022;112(1)43-47. https//doi.org/10.2105/AJPH.2021.306563).
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Contact Tracing / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: North America Language: English Journal: Am J Public Health Year: 2022 Document Type: Article Affiliation country: Ajph.2021.306563

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Contact Tracing / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: North America Language: English Journal: Am J Public Health Year: 2022 Document Type: Article Affiliation country: Ajph.2021.306563