The impact of early large-scale screening on the evolution of pandemics
研究了疫情早期资源有限时的大规模筛查策略，通过随机抽样和两种检测方法（个体与群体）控制疾病传播，并用美国COVID-19数据验证了减少感染和满足医疗容量的效果。
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.