A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions

Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher and Max Westphal
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Sonja Jäckle: Fraunhofer Institute for Digital Medicine MEVIS, 23562 Lübeck, Germany
Elias Röger: Fraunhofer Institute for Industrial Mathematics ITWM, 67663 Kaiserslautern, Germany
Volker Dicken: Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany
Benjamin Geisler: Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany
Jakob Schumacher: Health Department Berlin-Reinickendorf, 13407 Berlin, Germany
Max Westphal: Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany

IJERPH, 2021, vol. 18, issue 17, 1-13

Abstract: In Germany, local health departments are responsible for surveillance of the current pandemic situation. One of their major tasks is to monitor infected persons. For instance, the direct contacts of infectious persons at group meetings have to be traced and potentially quarantined. Such quarantine requirements may be revoked, when all contact persons obtain a negative polymerase chain reaction (PCR) test result. However, contact tracing and testing is time-consuming, costly and not always feasible. In this work, we present a statistical model for the probability that no transmission of COVID-19 occurred given an arbitrary number of negative test results among contact persons. Hereby, the time-dependent sensitivity and specificity of the PCR test are taken into account. We employ a parametric Bayesian model which combines an adaptable Beta-Binomial prior and two likelihood components in a novel fashion. This is illustrated for group events in German school classes. The first evaluation on a real-world dataset showed that our approach can support important quarantine decisions with the goal to achieve a better balance between necessary containment of the pandemic and preservation of social and economic life. Future work will focus on further refinement and evaluation of quarantine decisions based on our statistical model.

Keywords: COVID-19; decision support; bayesian statistics; quarantine; risk assessment (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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