The coronavirus disease 2019 (COVID-19) pandemic currently in process differs from other infectious disease calamities that have previously plagued humanity in the vast amount of information that is produced each day, which includes daily estimates of the disease incidence and mortality data. Apart from providing actionable information to public health authorities on the trend of the pandemic, the daily incidence reflects the process of disease in a susceptible population and thus reflects the pathogenesis of COVID-19, the public health response, and diagnosis and reporting. Both new daily cases and daily mortality data in the United States exhibit periodic oscillatory patterns. By analyzing New York City (NYC) and Los Angeles (LA) testing data, we demonstrate that this oscillation in the number of cases can be strongly explained by the daily variation in testing. This seems to rule out alternative hypotheses, such as increased infections on certain days of the week, as driving this oscillation. Similarly, we show that the apparent oscillation in mortality in the U.S. data are mostly an artifact of reporting, which disappears in data sets that record death by episode date, such as the NYC and LA data sets. Periodic oscillations in COVID-19 incidence and mortality data reflect testing and reporting practices and contingencies. Thus, these contingencies should be considered first prior to suggesting biological mechanisms.

中文翻译：

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美国COVID-19发病率和死亡率数据的波动反映了诊断和报告因素。

当前正在发生的2019年冠状病毒病（COVID-19）大流行与以前困扰人类的其他传染病灾难在每天产生的大量信息中有所不同，其中包括每天对疾病发病率和死亡率数据的估计。除了向公共卫生当局提供有关大流行趋势的可行信息外，每日发病率还反映了易感人群的疾病进程，因此反映了COVID-19的发病机理，公共卫生反应以及诊断和报告。在美国，新的每日病例和每日死亡率数据均表现出周期性的振荡模式。通过分析纽约市（NYC）和洛杉矶（LA）的测试数据，我们证明，这种情况的波动可以通过测试的每日变化来强烈解释。这似乎排除了其他假设，例如，在导致这种振荡的过程中，例如一周中某些天的感染增加。同样，我们表明，美国数据中死亡率的明显波动主要是报告的人为因素，在按事件日期记录死亡的数据集中（例如NYC和LA数据集）中消失了。COVID-19发病率和死亡率数据的周期性波动反映了测试和报告的做法以及突发事件。因此，在建议生物学机制之前应首先考虑这些偶然性。我们发现，美国数据中死亡率的明显波动主要是报告的人为因素，在按事件日期记录死亡的数据集中（例如NYC和LA数据集）消失了。COVID-19发病率和死亡率数据的周期性波动反映了测试和报告的做法以及突发事件。因此，在建议生物学机制之前应首先考虑这些偶然性。我们发现，美国数据中死亡率的明显波动主要是报告的人为因素，在按事件日期记录死亡的数据集中（例如NYC和LA数据集）消失了。COVID-19发病率和死亡率数据的周期性波动反映了测试和报告的做法以及突发事件。因此，在建议生物学机制之前应首先考虑这些偶然性。