Contact tracing for infectious disease models on complete graphs

Abstract

Contact tracing is a method that was used for the COVID-19 pandemic to reduce the transmission of the infectious disease. However, the actual impact of this measure is hard to quantify. In this paper we analyze a SIQR model, which overestimates the contact tracing rate. Furthermore, we create a stochastic model based on the Gillespie algorithm to analyze how accurate this overestimation is. Both models assume one-step contact tracing. We found a 37.5% reduction in peak total infection of the SIQR with CT model compared to the standard SIR model. The stochastic model has a 7.7% higher total infection peak, which can be attributed to the fact that the contact tracing rate is overestimated.