On the Effectiveness of Digital Contact Tracing and Contact Prevention Under Varying COVID-19 Infection Detection Rates

A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 spread. Due to its sustainability over time, contact tracing is one of the most frequently applied interventions worldwide, albeit with mixed results. In this work, we evaluate the perfomance of contact tracing for different infection detection rates and response time delays. We also introduce and analyze a novel strategy we call contact prevention. We model the effect of both strategies on contagion dynamics in SERIA, an agent-based simulation platform that implements realistic population-dependent statistical distributions. Results show that diagnostic/response time delays and low infection detection rates greatly impair the effect of contact tracing strategies, while contact prevention remains effective in these scenarios. Therefore, contact prevention could play a significant role in pandemic mitigation, specially in under-developed countries where diagnostic and tracing capabilities are insufficient. Contact prevention could thus sustainably reduce the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. Depending on infection detection and app adoption rates, applying a combination of contact tracing and contact prevention could reduce pandemic-related mortality by 20%-56%.