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The COVID-19 coronavirus pandemic has affected virtually every region of theglobe. At the time of conducting this study, the number of daily cases in theUnited States is more than any other country, and the trend is increasing inmost of its states. Google trends provide public interest in various topicsduring different periods. Analyzing these trends using data mining methodsmight provide useful insights and observations regarding the COVID-19 outbreak.The objective of this study was to consider the predictive ability of differentsearch terms (i.e., bars and restaurants) with regards to the increase of dailycases in the US. We considered the causation of two different search querytrends, namely restaurant and bars, on daily positive cases in top-10states/territories of the United States with the highest and lowest daily newpositive cases. In addition, to measure the linear relation of differenttrends, we used Pearson correlation. Our results showed for states/territorieswith higher numbers of daily cases, the historical trends in search queriesrelated to bars and restaurants, which mainly happened after re-opening,significantly affect the daily new cases, on average. California, for example,had most searches for restaurants on June 7th, 2020, which affected the numberof new cases within two weeks after the peak with the P-value of .004 forGranger's causality test. Although a limited number of search queries wereconsidered, Google search trends for restaurants and bars showed a significanteffect on daily new cases for regions with higher numbers of daily new cases inthe United States. We showed that such influential search trends could be usedas additional information for prediction tasks in new cases of each region.This prediction can help healthcare leaders manage and control the impact ofCOVID-19 outbreaks on society and be prepared for the outcomes.

The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis