Dynamic Modeling to Identify Mitigation Strategies for Covid-19 Pandemic

Abstract

Covid-19 mitigation commonly involves social distancing. Due to its high economic toll and its impact on personal freedom, we need to ease social distancing and deploy alternative measures, while preventing a second wave of infections. Bluetooth app-based contact tracing has been proposed, focusing on symptomatic cases and isolating their contacts. However, this approach would miss many transmissions by asymptomatic cases. To improve effectiveness of app-based mitigation, we propose to complement contact tracing with Smart Testing relying on Contact Counting (STeCC). STeCC focuses virus RNA testing to people with exceptionally high numbers of contacts. These people are at particularly high risk to become infected (with or without symptoms) and transmit the virus. Mathematical modeling shows that a mitigation strategy combining STeCC and contact tracing in one app will be more efficient than contact tracing and works when ≈50% (instead of ≥60%) of the total population participate. Similarly, it requires 50-100 fold less tests than randomized virus testing alone. These gains in efficiency may be critical for success. STeCC could be integrated in the current Bluetooth tracing apps. Thus, STeCC is technically feasible and can reduce the pandemic’s reproduction number by 2.4-fold (e.g. from R0=2.4 to Reff=1) with realistic test numbers (≈166 per 100’000 people per day), when a realistic fraction of the population would use the app (i.e. ≈50% in total population). Thereby, STeCC efficiently complements the portfolio of mitigation strategies, which allow easing social distancing without compromising public health.