A Non-Markovian Model to Assess Contact Tracing for the Containment of COVID-19

Non-pharmaceutical interventions, such as contact tracing has been an important tool in controlling epidemic outbreaks. In this paper, we propose a non-Markovian, network-based mathematical model to assess the effectiveness of contact tracing in COVID-19 containment. To improve the reliability of the model, empirically determined distributions were incorporated for the sojourn times of the model's states. The first-order closure approximation was used to derive an expression for the epidemic threshold. Using survey contact data collected during the 2020 fall academic semester from a university population, we determined that even four to five close contacts were sufficient to maintain the viral transmission. Additionally, our model reveals that contact tracing can be an effective outbreak mitigation measure by reducing the epidemic size by more than three-fold. Furthermore, we show that our proposed model, which accounts for the underlying complexities of the COVID-19 spreading process, performs better in short-term forecasts of case counts.