A statistical model to assess risk for supporting SARS-CoV-2 quarantine decisions

In February 2020 the first human infection with SARS-CoV-2 was reported in Germany. Since then the local public health offices have been responsible to monitor and react to the dynamics of the pandemic. One of their major tasks is to contain the spread of the virus after potential spreading events, for example when one or multiple participants have a positive test result after a group meeting (e.g. at school, at a sports event or at work). In this case, contacts of the infected person have to be traced and potentially are quarantined (at home) for a period of time. When all relevant contact persons obtain a negative polymerase chain reaction (PCR) test result, the quarantine may be stopped. However, tracing and testing of all contacts is time-consuming, costly and (thus) not always feasible. This motivates our work, in which we present a statistical model for the probability that no transmission of Sars-CoV-2 occurred given an arbitrary number of test results at potentially different timepoints. Hereby, the time-dependent sensitivity and specificity of the conducted PCR test are taken in account. We employ a parametric Bayesian model which can be adopted to different situations when specific prior knowledge is available. This is illustrated for group events in German school classes and applied to exemplary real-world data from this context. Our approach has the potential to 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. The focus of future work should be on further refinement and evaluation of quarantine decisions based on our statistical model.