EsteR – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities

Author:

Jäckle Sonja1,Alpers Rieke1,Kühne Lisa2,Schumacher Jakob3,Geisler Benjamin1,Westphal Max1

Affiliation:

1. Fraunhofer Institute for Digital Medicine MEVIS, Bremen / Lübeck, Germany

2. Leibniz Institute for Preventive Research and Epidemiology BIPS, Bremen, Germany

3. Local Health Authority Berlin-Reinickendorf, Berlin, Germany

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.

Publisher

IOS Press

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