Modeling the Spread of COVID-19 in Response to Various Surveillance Testing Strategies

Since early March 2020, government agencies have utilized a wide variety of non-pharmaceutical interventions to mitigate the spread of COVID-19. At many universities, fundamental issues relating both to the spread of the disease and the ability of administrators to respond to a sudden rise in cases remain. Surveillance testing strategies have been implemented in places that have reopened, to simultaneously monitor community spread and isolate discovered cases. On college campuses, the question remains as to what kind of testing is required to remain safely open. Here, we propose an extension of the SIR model to investigate the effectiveness of various techniques that have been used throughout the pandemic to slow the spread of COVID-19 on college campuses. In particular, we present a minimal mathematical model that includes time-varying testing strategies viewed as a control. We use numerical simulations to show how testing strategies may change in response to averaged disease transmission rates, where such rates are governed by university-specific guidelines (e.g. classroom sizes, ventilation, etc). We show that surveillance testing can be effective if isolation guidelines are followed. Further testing strategies are presented which are more robust to either non-compliance of distancing mandates, or so-called 'superspreader' events.