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首页> 外文期刊>JMIR public health and surveillance. >The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis
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The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis

机译：美国餐馆和酒吧的公共利益因果关系推理 - 美国的日常科幻 - 19例：谷歌趋势分析

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Background The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. Objective The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. Methods To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. Results Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. Conclusions Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.
机译：背景技术Covid-19大流行已经影响了世界上每个地区。在这项研究的时候，美国日常新案例的数量大于任何其他国家的新案例，大多数州的趋势都在增加。谷歌趋势在不同时期内提供有关公共利益的数据。使用数据挖掘方法分析这些趋势可以提供关于Covid-19爆发的有用的见解和观察。目的本研究的目的是考虑不同搜索条款与Covid-19没有直接相关的预测能力，关于美国日常案件的增加。特别是，我们涉及与用餐餐馆和酒吧相关的搜索。从Google趋势应用程序编程接口和Covid-19跟踪项目获得数据。方法要在另一个时序列的原因测试原因，我们使用了Granger因果关系测试。我们认为两种不同的搜索查询趋势与美国国家和地区日常积极案例中的用餐和酒吧有关，其中10个最高和10个最低数量的Covid-19。此外，我们使用Pearson相关性来测量不同趋势之间的线性关系。结果我们的研究结果表明，对于日常案件数量较多的国家和地区，与酒吧和餐馆相关的搜索查询中的历史趋势，主要发生在重新开放后，显着影响日常新病例的数量。例如，加利福尼亚州为2020年6月7日展示了最多的餐馆搜索;这影响了峰值后两周内的新病例数量，对于Ganger因果关系测试，P值为.004。结论虽然考虑了有限数量的搜索查询，但餐馆和酒吧的谷歌搜索趋势对美国各国和地区的日常新案件产生了重大影响，每日新案例较多。我们认为，这些有影响力的搜索趋势可用于为关于每个区域的新案例提供预测任务的其他信息。这些预测可以帮助医疗领导人管理和控制Covid-19爆发对社会的影响，并为其结果做好准备。

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