Preventing COVID-19 Outbreaks Through Surveillance Testing in Healthcare Facilities - A Modelling Study

ABSTRACT

Surveillance testing within healthcare facilities provides an opportunity to prevent severe outbreaks of coronavirus disease 2019 (COVID-19). However, the quantitative impact of different available surveillance strategies is not well-understood. Our study adds to the available body of evidence by examining different strategies for their potential to decrease the probability of outbreaks in these facilities. Based on our findings, we propose determinants of successful surveillance measures. To this end, we establish an individual-based model representative of a mental health hospital yielding generalizable results. Attributes and features of this facility were derived from a prototypical hospital, which provides psychiatric, psychosomatic and psychotherapeutic treatment. We estimate the relative reduction of outbreak probability for three test strategies (entry test, once-weekly test and twice-weekly test) relative to a symptom-based baseline strategy. We found that fast diagnostic test results and adequate compliance of the clinic population are mandatory for conducting effective surveillance. The robustness of these results towards uncertainties is demonstrated via comprehensive sensitivity analyses. In summary, we robustly quantified the efficacy of different surveillance scenarios and conclude that active testing in mental health hospitals and similar facilities successfully reduces the number of COVID-19 outbreaks.