Literature DB >> 34343467

Suitability of Google Trends™ for digital surveillance during ongoing COVID-19 epidemic: a case study from India.

Parmeshwar Satpathy1, Sanjeev Kumar2, Pankaj Prasad2.   

Abstract

OBJECTIVE: Digital surveillance has shown mixed results as supplement to traditional surveillance. Google Trends™ (GT) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India.
METHODS: We obtained data on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behaviour in India between 1st January and 31st May 2020 in the form of relative search volume (RSV). We used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests.
RESULTS: GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms "COVID 19", "COVID", "social distancing", "soap" and "lockdown" at national level. In five high-burden states, high correlation was observed for these five terms along with "Corona". Peaks in RSV both at national level and high-burden states corresponded with media coverage or government declarations on the ongoing pandemic.
CONCLUSION: The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage induced curiosity or health-seeking.

Entities:  

Keywords:  ICT in healthcare; disease surveillance; infodemiology; pandemic; time lag correlation

Year:  2021        PMID: 34343467     DOI: 10.1017/dmp.2021.249

Source DB:  PubMed          Journal:  Disaster Med Public Health Prep        ISSN: 1935-7893            Impact factor:   1.385


  3 in total

1.  COVID-19 and thyroid disease: An infodemiological pilot study.

Authors:  Ioannis Ilias; Charalampos Milionis; Eftychia Koukkou
Journal:  World J Methodol       Date:  2022-05-20

Review 2.  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

3.  Modeling COVID-19 incidence with Google Trends.

Authors:  Lateef Babatunde Amusa; Hossana Twinomurinzi; Chinedu Wilfred Okonkwo
Journal:  Front Res Metr Anal       Date:  2022-09-15
  3 in total

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