Literature DB >> 32629121

Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in US Hotspots.

Imama Ahmad1, Ryan Flanagan2, Kyle Staller3.   

Abstract

Google Trends is an online tool that allows measurement of search term popularity on Google, spatially and temporally. While not an epidemiological tool for determining incidence, it can estimate the popularity of a certain disease by search volume over time.1,2 It has previously correlated well with infectious disease incidence and has demonstrated utility in disease forecasting, especially with influenza data.3 We utilized Google Trends to investigate whether search interest in common gastrointestinal (GI) symptoms would correlate with coronavirus disease 2019 (COVID-19) incidence data.
Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.

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Year:  2020        PMID： 32629121     DOI： 10.1016/j.cgh.2020.06.058

Source DB:  PubMed          Journal:  Clin Gastroenterol Hepatol        ISSN： 1542-3565            Impact factor:   11.382


  9 in total

1.  Changes in National Google Trends and Local Healthcare Utilization After High-Impact Gastroenterology Publications.

Authors:  Amrit K Kamboj; Siddharth Agarwal; Victor G Chedid; Prasad G Iyer; Kent R Bailey; William S Harmsen; David A Katzka
Journal:  Am J Gastroenterol       Date:  2021-12-01       Impact factor: 10.864

2.  Using Google Health Trends to investigate COVID-19 incidence in Africa.

Authors:  Alexander Fulk; Daniel Romero-Alvarez; Qays Abu-Saymeh; Jarron M Saint Onge; A Townsend Peterson; Folashade B Agusto
Journal:  PLoS One       Date:  2022-06-07       Impact factor: 3.752

3.  COVID-19 Epidemiology and Google Searches.

Authors:  Claire L Jansson-Knodell; Indira Bhavsar-Burke; Andrea Shin
Journal:  Clin Gastroenterol Hepatol       Date:  2020-10-15       Impact factor: 11.382

4.  Non-intrusive wastewater surveillance for monitoring of a residential building for COVID-19 cases.

Authors:  Judith Chui Ching Wong; Joanna Tan; Ying Xian Lim; Sathish Arivalan; Hapuarachchige Chanditha Hapuarachchi; Diyar Mailepessov; Jane Griffiths; Praveena Jayarajah; Yin Xiang Setoh; Wei Ping Tien; Swee Ling Low; Carmen Koo; Surya Pavan Yenamandra; Marcella Kong; Vernon Jian Ming Lee; Lee Ching Ng
Journal:  Sci Total Environ       Date:  2021-04-29       Impact factor: 7.963

5.  #MaskOn! #MaskOff! Digital polarization of mask-wearing in the United States during COVID-19.

Authors:  Jun Lang; Wesley W Erickson; Zhuo Jing-Schmidt
Journal:  PLoS One       Date:  2021-04-28       Impact factor: 3.240

6.  An open repository of real-time COVID-19 indicators.

Authors:  Alex Reinhart; Logan Brooks; Maria Jahja; Aaron Rumack; Jingjing Tang; Sumit Agrawal; Wael Al Saeed; Taylor Arnold; Amartya Basu; Jacob Bien; Ángel A Cabrera; Andrew Chin; Eu Jing Chua; Brian Clark; Sarah Colquhoun; Nat DeFries; David C Farrow; Jodi Forlizzi; Jed Grabman; Samuel Gratzl; Alden Green; George Haff; Robin Han; Kate Harwood; Addison J Hu; Raphael Hyde; Sangwon Hyun; Ananya Joshi; Jimi Kim; Andrew Kuznetsov; Wichada La Motte-Kerr; Yeon Jin Lee; Kenneth Lee; Zachary C Lipton; Michael X Liu; Lester Mackey; Kathryn Mazaitis; Daniel J McDonald; Phillip McGuinness; Balasubramanian Narasimhan; Michael P O'Brien; Natalia L Oliveira; Pratik Patil; Adam Perer; Collin A Politsch; Samyak Rajanala; Dawn Rucker; Chris Scott; Nigam H Shah; Vishnu Shankar; James Sharpnack; Dmitry Shemetov; Noah Simon; Benjamin Y Smith; Vishakha Srivastava; Shuyi Tan; Robert Tibshirani; Elena Tuzhilina; Ana Karina Van Nortwick; Valérie Ventura; Larry Wasserman; Benjamin Weaver; Jeremy C Weiss; Spencer Whitman; Kristin Williams; Roni Rosenfeld; Ryan J Tibshirani
Journal:  Proc Natl Acad Sci U S A       Date:  2021-12-21       Impact factor: 12.779

7.  Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study.

Authors:  Atina Husnayain; Eunha Shim; Anis Fuad; Emily Chia-Yu Su
Journal:  J Med Internet Res       Date:  2021-12-22       Impact factor: 5.428

8.  Interest in Home Birth During the COVID-19 Pandemic: Analysis of Google Trends Data.

Authors:  Ru-Fong J Cheng; Alan C Fisher; Susan C Nicholson
Journal:  J Midwifery Womens Health       Date:  2022-03-10       Impact factor: 2.891

Review 9.  Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.

Authors:  Tobias Saegner; Donatas Austys
Journal:  Int J Environ Res Public Health       Date:  2022-09-29       Impact factor: 4.614
  9 in total

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