Google Trends Analysis of COVID-19

• URL: http://arxiv.org/abs/2011.03847v1
• Date: Sat, 7 Nov 2020 20:55:19 GMT
• Title: Google Trends Analysis of COVID-19
• Authors: Hoang Long Nguyen, Zhenhe Pan, Hashim Abu-gellban, Fang Jin, Yuanlin Zhang
• Abstract summary: 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.
• Score: 3.1277175082738005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The World Health Organization (WHO) announced that COVID-19 was a pandemic disease on the 11th of March as there were 118K cases in several countries and territories. Numerous researchers worked on forecasting the number of confirmed cases since anticipating the growth of the cases helps governments adopting knotty decisions to ease the lockdowns orders for their countries. These orders help several people who have lost their jobs and support gravely impacted businesses. Our research aims to investigate the relation between Google search trends and the spreading of the novel coronavirus (COVID-19) over countries worldwide, to predict the number of cases. We perform a correlation analysis on the keywords of the related Google search trends according to the number of confirmed cases reported by the WHO. After that, we applied several machine learning techniques (Multiple Linear Regression, Non-negative Integer Regression, Deep Neural Network), to forecast the number of confirmed cases globally based on historical data as well as the hybrid data (Google search trends). Our results show that Google search trends are highly associated with the number of reported confirmed cases, where the Deep Learning approach outperforms other forecasting techniques. We believe that it is not only a promising approach for forecasting the confirmed cases of COVID-19, but also for similar forecasting problems that are associated with the related Google trends.

Related papers

• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Twitter conversations predict the daily confirmed COVID-19 cases [0.2320417845168326]
  Pandemic-specific discourse has remained on-trend on microblogging platforms such as Twitter and Weibo. We propose a sentiment-involved topic-based methodology for designing multiple time series from publicly available COVID-19 related Twitter conversations. We show that the inclusion of social media variables for modeling introduces 48.83--51.38% improvements on RMSE over the baseline models.
  arXiv  Detail & Related papers  (2022-06-21T15:31:06Z)
• COVID-19 Hospitalizations Forecasts Using Internet Search Data [4.748730334762718]
  We extend previously-proposed influenza tracking model, ARGO, to predict future 2-week national and state-level COVID-19 new hospital admissions. Our method achieves on average 15% error reduction over the best alternative models collected from COVID-19 forecast hub.
  arXiv  Detail & Related papers  (2022-02-03T21:56:20Z)
• COVID-19 cases prediction using regression and novel SSM model for non-converged countries [0.0]
  The new associated cases with COVID-19 information were gathered from 20 January 2020 to 21 July 2020. We utilized the SARIMAX, Linear regression model to anticipate new suspected COVID-19 cases for the countries which did not converge yet. Our framework can foresee peak corona cases with an R-Squared value of 0.986 utilizing linear regression and fall of this pandemic at various levels for countries like India, US, and Brazil.
  arXiv  Detail & Related papers  (2021-06-04T13:02:08Z)
• FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
  COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19. The data itself is still scarce due to patient privacy concerns. We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
  arXiv  Detail & Related papers  (2021-02-10T01:56:58Z)
• 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)
• The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis [2.826858247883636]
  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.
  arXiv  Detail & Related papers  (2020-07-27T00:29:06Z)
• 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)
• 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)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

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.