We propose, implement, and evaluate a method to estimate the daily number of new symptomatic COVID-19 infections, at the level of individual U.S. counties, by deconvolving daily reported COVID-19 case counts using an estimated symptom-onset-to-case-report delay distribution. Importantly, we focus on estimating infections in real-time (rather than retrospectively), which poses numerous challenges. To address these, we develop new methodology for both the distribution estimation and deconvolution steps, and we employ a sensor fusion layer (which fuses together predictions from models that are trained to track infections based on auxiliary surveillance streams) in order to improve accuracy and stability.

翻译：我们建议、实施和评价一种方法,用估计的症状对个案报告迟发分布,将每日报告的COVID-19病例数与每日报告的COVID-19病例数脱钩,从而估计美国各州的COVID-19新感染病例每日新增病例数。重要的是,我们注重实时(而不是追溯性)感染估计,这带来了许多挑战。为了解决这些问题,我们为分配估计和分解步骤制定了新方法,我们采用了一个传感器聚变层(结合根据辅助监测流对传染病进行跟踪的模型所作的预测),以提高准确性和稳定性。