During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

在全球所有国家因2019年冠状病毒病（COVID-19）大流行而面临的前所未有的情况下，该流行病也造成了严重的社会经济后果，当务之急是探索新颖的方法来监测和预测区域性暴发或甚至在他们这样做之前。为此，本文介绍了Google查询数据在美国国家和州一级在COVID-19的可预测性中的作用。作为初步调查，将检查Pearson和Kendall等级相关性，以探究Google趋势数据与COVID-19病例和死亡数据之间的关系。接下来，执行COVID-19可预测性分析，采用的模型是分位数回归，该分位数回归是通过自举模拟进行偏差校正的，即 强大的回归分析，这是应对样本中异常值的一种适当的统计方法，同时还可以减轻样本估计偏差。结果表明，Google趋势和COVID-19数据之间存在统计上的显着相关性，而估计的模型显示出很强的COVID-19可预测性。与先前的工作表明在线实时数据在流行病和暴发的监测和预测中很有价值的研究相一致，显然，这种信息流行病学方法可以帮助公共卫生政策制定者解决最关键的问题：弄平曲线，分配卫生资源，并提高其各自卫生保健系统的有效性和准备性。