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

• URL: http://arxiv.org/abs/2007.13255v1
• Date: Mon, 27 Jul 2020 00:29:06 GMT
• Title: The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis
• Authors: Milad Asgari Mehrabadi, Nikil Dutt and Amir M. Rahmani
• Abstract summary: The COVID-19 coronavirus pandemic has affected virtually every region of the globe. The number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods.
• Score: 2.826858247883636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to the increase of daily cases in the US. We considered the causation of two different search query trends, namely restaurant and bars, on daily positive cases in top-10 states/territories of the United States with the highest and lowest daily new positive cases. In addition, to measure the linear relation of different trends, we used Pearson correlation. Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related 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 number of new cases within two weeks after the peak with the P-value of .004 for Granger's causality test. Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases for regions with higher numbers of daily new cases in the United States. We showed that such influential search trends could be used as additional information for prediction tasks in new cases of each region. This prediction can help healthcare leaders manage and control the impact of COVID-19 outbreaks on society and be prepared for the outcomes.

Related papers

• Exploring the impact of social stress on the adaptive dynamics of COVID-19: Typing the behavior of na\"ive populations faced with epidemics [43.50312332512221]
  The COVID-19 pandemic has brought to light profound variations among different countries in terms of their adaptive dynamics. This emphasizes the crucial role of cultural characteristics in natural disaster analysis.
  arXiv  Detail & Related papers  (2023-11-23T11:05:54Z)
• American Twitter Users Revealed Social Determinants-related Oral Health Disparities amid the COVID-19 Pandemic [72.44305630014534]
  We collected oral health-related tweets during the COVID-19 pandemic from 9,104 Twitter users across 26 states. Women and younger adults (19-29) are more likely to talk about oral health problems. People from counties at a higher risk of COVID-19 talk more about tooth decay/gum bleeding and chipped tooth/tooth break.
  arXiv  Detail & Related papers  (2021-09-16T01:10:06Z)
• Twitter Subjective Well-Being Indicator During COVID-19 Pandemic: A Cross-Country Comparative Study [0.0]
  This study analyzes the impact of the COVID-19 pandemic on the subjective well-being as measured through Twitter data indicators for Japan and Italy. Overall, the subjective well-being dropped by 11.7% for Italy and 8.3% for Japan in the first nine months of 2020 compared to the last two months of 2019.
  arXiv  Detail & Related papers  (2021-01-19T15:51:53Z)
• Google Trends Analysis of COVID-19 [3.1277175082738005]
  The World Health Organization (WHO) announced that COVID-19 was a pandemic disease on the 11th of March. Our research aims to investigate the relation between Google search trends and the spreading of the novel coronavirus.
  arXiv  Detail & Related papers  (2020-11-07T20:55:19Z)
• Surveillance of COVID-19 Pandemic using Hidden Markov Model [0.0]
  We look at applying Hidden Markov Model to get a better assessment of extent of spread. The data we have chosen to analyze pertains to Indian scenario.
  arXiv  Detail & Related papers  (2020-08-14T05:45:34Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data [63.15776078733762]
  We propose Amortized Causal Discovery, a novel framework to learn to infer causal relations from time-series data. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance.
  arXiv  Detail & Related papers  (2020-06-18T19:59:12Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• The Pace and Pulse of the Fight against Coronavirus across the US, A Google Trends Approach [0.0]
  Google Trends can be used as a proxy for what people are thinking, needing, and planning. We use it to provide both insights into, and potential indicators of, important changes in information-seeking patterns during pandemics like COVID-19.
  arXiv  Detail & Related papers  (2020-05-05T20:55:45Z)
• Observed mobility behavior data reveal social distancing inertia [16.89576620375838]
  The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have all showed an unexpected trend. The trends showed that as soon as COVID-19 cases were observed, the statistics started improving, regardless of government actions. The study suggests that there is a natural behavior inertia toward social distancing, which puts a limit on the extent of improvement in the social-distancing-related statistics.
  arXiv  Detail & Related papers  (2020-04-30T13:12:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.