The impact of early large-scale screening on the evolution of pandemics

Abstract

We study the problem of large-scale screening in the early stages of a pandemic. In this setting, resources such as testing kits, budget, and hospital beds are scarce, and testing in early stages has the potential to control the dynamics of disease spread. We thus devise a screening framework in which subjects are randomly sampled from the population for screening. We investigate two screening strategies - individual and group testing - and identify settings in which each is more effective. To model disease spread dynamics, we utilize a compartmental susceptible-infected-quarantined-removed-deceased model in which we embed sampling and screening decisions that account for testing misclassification errors. We perform a sensitivity analysis over the parameters that govern the disease dynamics and show the efficacy of our proposed screening solutions. We then calibrate our model using data on the COVID-19 pandemic in the United States and demonstrate the effectiveness of our model in reducing total number of infections while meeting healthcare system capacity.