Effective monitoring of infectious disease incidence remains a major challenge to public health. Difficulties in estimating the trends in disease incidence arise mainly from the time delay between case diagnosis and the reporting of cases to public health databases. However, predictive models usually assume that public data sets faithfully reflect the state of disease transmission. In this paper, we study the effect of delayed case reporting by comparing data reported by the Johns Hopkins Coronavirus Resource Center (CRC) with that of the raw clinical data collected from the San Antonio Metro Health District (SAMHD), San Antonio, Texas. An insight on the subtle effect that such reporting errors potentially have on predictive modeling is presented. We use an exponential distribution model for the regression analysis of the reporting delay. The proposed model for correcting reporting delays was applied to our recently developed SEYAR (Susceptible, Exposed, Symptomatic, Asymptomatic, Recovered) dynamical model for COVID-19 transmission dynamics. Employing data from SAMHD, we demonstrate that the forecasting ability of the SEYAR model is substantially improved when the rectified reporting obtained from our proposed model is utilized. The methods and findings demonstrated in this work have ample applicability in the forecasting of infectious disease outbreaks. Our findings suggest that failure to consider reporting delays in surveillance data can significantly alter forecasts.

中文翻译：

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数据修正，以解决报告疾病发病率的延迟问题，并应用预测 COVID-19 病例

有效监测传染病发病率仍然是公共卫生的重大挑战。估计疾病发病率趋势的困难主要源于病例诊断和向公共卫生数据库报告病例之间的时间延迟。然而，预测模型通常假设公共数据集忠实地反映了疾病传播的状态。在本文中，我们通过比较约翰·霍普金斯大学冠状病毒资源中心 (CRC) 报告的数据与从德克萨斯州圣安东尼奥市圣安东尼奥都会卫生区 (SAMHD) 收集的原始临床数据来研究延迟病例报告的影响。提出了有关此类报告错误可能对预测建模产生的微妙影响的见解。我们使用指数分布模型对报告延迟进行回归分析。所提出的纠正报告延迟的模型适用于我们最近开发的 SEYAR（易感、暴露、有症状、无症状、恢复）COVID-19 传播动态模型。利用 SAMHD 的数据，我们证明，当使用从我们提出的模型获得的修正报告时，SEYAR 模型的预测能力得到了显着提高。这项工作中证明的方法和发现在预测传染病爆发方面具有广泛的适用性。我们的研究结果表明，不考虑监测数据报告延迟可能会显着改变预测。