At the time of this article, COVID-19 has been transmitted to more than 42 million people and resulted in more than 673,000 deaths across the United States. Throughout this pandemic, public health authorities have monitored the results of diagnostic testing to identify hotspots of transmission. Such information can help reduce or block transmission paths of COVID-19 and help infected patients receive early treatment. However, most current schemes of test site allocation have been based on experience or convenience, often resulting in low efficiency and non-optimal allocation. In addition, the historical sociodemographic patterns of populations within cities can result in measurable inequities in access to testing between various racial and income groups. To address these pressing issues, we propose a novel test site allocation scheme to (a) maximize population coverage, (b) minimize prediction uncertainties associated with projections of outbreak trajectories, and (c) reduce inequities in access. We illustrate our approach with case studies comparing our allocation scheme with recorded allocation of testing sites in Georgia, revealing increases in both population coverage and improvements in equity of access over current practice.
翻译:在撰写本篇文章时,COVID-19已传送给4 200多万人,造成全美国673 000多人死亡;在整个这一大流行病期间,公共卫生当局监测诊断检测结果,以查明传染热点;这种信息有助于减少或阻断COVID-19的传播途径,帮助受感染病人获得早期治疗;然而,目前大多数试验地点分配计划都是基于经验或方便,往往导致效率低和非最佳分配;此外,城市内人口的历史社会人口模式可能导致不同种族和收入群体之间在获得检测方面出现可衡量的不平等;为解决这些紧迫问题,我们提议一个新的试验地点分配计划,以便(a) 最大限度地扩大人口覆盖面,(b) 尽量减少与预测爆发轨迹有关的预测不确定性,(c) 减少获取机会方面的不平等。我们举例说明了我们的个案研究方法,将我们的分配计划与记录在格鲁吉亚的试验地点分配情况进行比较,表明人口覆盖面的扩大和获得公平性高于现行做法。