{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:38:56Z","timestamp":1768347536062,"version":"3.49.0"},"reference-count":12,"publisher":"Georg Thieme Verlag KG","issue":"04","license":[{"start":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T00:00:00Z","timestamp":1633478400000},"content-version":"vor","delay-in-days":66,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003The dramatic increase in complexity and volume of health data has challenged traditional health systems to deliver useful information to their users. The novel coronavirus disease 2019 (COVID-19) pandemic has further exacerbated this problem and demonstrated the critical need for the 21st century approach. This approach needs to ingest relevant, diverse data sources, analyze them, and generate appropriate health intelligence products that enable users to take more effective and efficient actions for their specific challenges.<\/jats:p><jats:p>\n          Objectives\u2003This article characterizes the Health Intelligence Atlas (HI-Atlas) development and implementation to produce Public Health Intelligence (PHI) that supports identifying and prioritizing high-risk communities by public health authorities. The HI-Atlas moves from post hoc observations to a proactive model-based approach for preplanning COVID-19 vaccine preparedness, distribution, and assessing the effectiveness of those plans.<\/jats:p><jats:p>\n          Results\u2003Details are presented on how the HI-Atlas merged traditional surveillance data with social intelligence multidimensional data streams to produce the next level of health intelligence. Two-model use cases in a large county demonstrate how the HI-Atlas produced relevant PHI to inform public health decision makers to (1) support identification and prioritization of vulnerable communities at risk for COVID-19 spread and vaccine hesitancy, and (2) support the implementation of a generic model for planning equitable COVID-19 vaccine preparedness and distribution.<\/jats:p><jats:p>\n          Conclusion\u2003The scalable models of data sources, analyses, and smart hybrid data layer visualizations implemented in the HI-Atlas are the Health Intelligence tools designed to support real-time proactive planning and monitoring for COVID-19 vaccine preparedness and distribution in counties and states.<\/jats:p>","DOI":"10.1055\/s-0041-1735973","type":"journal-article","created":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T01:25:35Z","timestamp":1633569935000},"page":"944-953","source":"Crossref","is-referenced-by-count":10,"title":["Health Intelligence Atlas: A Core Tool for Public Health Intelligence"],"prefix":"10.1055","volume":"12","author":[{"given":"Gabriela M.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Multi-Interprofessional Center for Health Informatics, The University of Texas at Arlington, Arlington, Texas, United States"}]},{"given":"Marion J.","family":"Ball","sequence":"additional","affiliation":[{"name":"Multi-Interprofessional Center for Health Informatics, The University of Texas at Arlington, Arlington, Texas, United States"}]},{"given":"Peter","family":"Szczesny","sequence":"additional","affiliation":[{"name":"Public Affairs, RAD Team, Ipsos, New York, New York, United States"}]},{"given":"Samuel","family":"Haymann","sequence":"additional","affiliation":[{"name":"Public Affairs, RAD Team, Ipsos, New York, New York, United States"}]},{"given":"Mark","family":"Polyak","sequence":"additional","affiliation":[{"name":"Global Public Affairs, Ipsos, Washington, District of Columbia, United States"}]},{"given":"Talmage","family":"Holmes","sequence":"additional","affiliation":[{"name":"Tarrant County Public Health, Fort Worth, Texas, United States"}]},{"given":"John S.","family":"Silva","sequence":"additional","affiliation":[{"name":"Multi-Interprofessional Center for Health Informatics, The University of Texas at Arlington, Arlington, Texas, United States"}]}],"member":"194","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"issue":"01","key":"ref2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/09540962.2020.1764206","article-title":"New development: COVID-19 as an accelerator of digital transformation in public service delivery","volume":"41","author":"D Agostino","year":"2021","journal-title":"Public Money Manag"},{"issue":"11","key":"ref3","doi-asserted-by":"crossref","first-page":"e0242476","DOI":"10.1371\/journal.pone.0242476","article-title":"The impacts of COVID-19 pandemic on public transit demand in the United 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W Allard","year":"2017"}],"container-title":["Applied Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/s-0041-1735973.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T01:26:28Z","timestamp":1633569988000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/s-0041-1735973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":12,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2021,8,4]]},"published-print":{"date-parts":[[2021,8]]}},"URL":"https:\/\/doi.org\/10.1055\/s-0041-1735973","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["1869-0327"],"issn-type":[{"value":"1869-0327","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8]]}}}