Optimal Testing Strategy for the Identification of COVID-19 Infections

Abstract

The systematic identification of infectious, yet unreported, individuals is critical for the containment of the COVID-19 pandemic. We present a strategy for identifying the location, timing and extent of testing that maximizes information gain for such infections. The optimal testing strategy relies on Bayesian experimental design and forecasting epidemic models that account for time dependent interventions. It is applicable at the onset and spreading of the epidemic and can forewarn for a possible recurrence of the disease after relaxation of interventions. We examine its application in Switzerland and show that it can provide timely and systematic guidance for the effective identification of infectious individuals with finite testing resources. The methodology and the open source code are readily adaptable to countries around the world.

How optimal allocation of limited testing capacity changes epidemic dynamics

“…The limited work that has been done on this topic has emerged recently, with some efforts focusing on using pooled testing as a simple means to stretch limited testing capacity as far as possible (Aragón-Caqueo et al, 2020; de Wolff et al, 2020; Ghosh et al, 2020; Gollier and Gossner, 2020; Jonnerby et al, 2020), while others consider stratified testing strategies focused on high risk groups such as health care workers (Cleevely et al, 2020; Grassly et al, 2020). Mathematical optimization has been applied to the economics of lockdown and quarantine policies (Aldila et al, 2020; Alvarez et al, 2020; Choi and Shim, 2021; Jones et al, 2020; Khatua et al, 2020; Piguillem and Shi, 2020), and to parameter estimation using testing data (Chatzimanolakis et al, 2020), but has not yet been applied comprehensively to resource allocation problems under testing constraints. Faced with insufficient testing capacity, public health agencies advise the prioritization of testing effort via qualitative considerations (Centers for Disease Control and Prevention, 2020).…”