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EsteR - A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities.
Jäckle, Sonja; Alpers, Rieke; Kühne, Lisa; Schumacher, Jakob; Geisler, Benjamin; Westphal, Max.
  • Jäckle S; Fraunhofer Institute for Digital Medicine MEVIS, Bremen / Lübeck, Germany.
  • Alpers R; Fraunhofer Institute for Digital Medicine MEVIS, Bremen / Lübeck, Germany.
  • Kühne L; Leibniz Institute for Preventive Research and Epidemiology BIPS, Bremen, Germany.
  • Schumacher J; Local Health Authority Berlin-Reinickendorf, Berlin, Germany.
  • Geisler B; Fraunhofer Institute for Digital Medicine MEVIS, Bremen / Lübeck, Germany.
  • Westphal M; Fraunhofer Institute for Digital Medicine MEVIS, Bremen / Lübeck, Germany.
Stud Health Technol Inform ; 296: 17-24, 2022 Aug 17.
Article in English | MEDLINE | ID: covidwho-2022596
ABSTRACT
In Germany, the current COVID-19 cases are managed and reported by the local health authorities. The workload of their employees during the pandemic is high, especially in periods of high infection numbers. In this work a decision support toolkit for local health authorities is introduced. A demonstrator web application was developed with the R Shiny framework and is publicly accessible online. It contains five separate tools based on statistical models for specific use cases and corresponding questions of COVID-19 cases and their contacts. The underlying statistical methods have been implemented in a new open-source R package. The toolkit has the potential to support local health authorities' employees in their daily work. A simulated-based validation of the statistical models and a usability evaluation of the demonstrator application in a user study will be carried out in the future.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220799

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220799