Evidence map›Paper›PMID 35881584›Full record

ArticlePloS one2022

Assessing the online search behavior for COVID-19 outbreak: Evidence from Iran.

Mahnaz Samadbeik, Ali Garavand, Nasim Aslani, Farzad Ebrahimzadeh, Farhad Fatehi

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Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review.International journal of environmental research and public health · 2022
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Mahnaz SamadbeikSocial Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran.
Ali GaravandSocial Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran.ORCID 0000-0003-2238-0510
Nasim AslaniSocial Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran.
Farzad EbrahimzadehDepartment of Biostatistics and Epidemiology, School of Health and Nutrition, Nutritional Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran.
Farhad FatehiSchool of Psychological Sciences, Monash University, Melbourne, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGoogle Trends (GT) is an important free tool for online search behavior analysis, which provides access to Internet search patterns in Google. In recent decades, this database has been used for predicting the outbreak of epidemics and pandemics in different regions of the world. The present study aimed to evaluate Iranian users' COVID-19-related online search behavior.

methodsThis longitudinal study was conducted in 2021. The data of Iranian users' COVID-19-related online search behavior (trend) were collected from the GT website, and the epidemiological data of the COVID-19 outbreak in Iran from 16 February 2020 to 2 January 2021 were sourced from the Iranian ministry of health and medical education, as well as the World Health Organization. The data were analyzed in SPSS using descriptive and inferential statistics.

resultsAll the COVID-19-related search terms in Iran gained their highest popularity value (relative search volume = 100) in the first 8 weeks of the pandemic, and then this value assumed a decreasing trend over time. Based on factor analysis, relative search volume (RSV) of factor 1 terms (related to corona [in Persian] and corona) have a low significance relationship with COVID-19 epidemiological data in one-, two-, and three-week time lags. Although, RSV of factor 2 terms (related to COVID [in Persian], COVID-19, and coronavirus) correlated with the total weekly number of COVID-19 cases in mentioned time lags.

conclusionCOVID-19-related search terms were popular among Iranian users at the beginning of the pandemic. The online search queries and the key terms searched by Iranian users varied during the COVID-19 pandemic. This study provides evidence in favor of the adoption of GT as an epidemiological surveillance tool but, it is necessary to consider that mass media and other confounders can significantly influence RSVs.

Indexed as

COVID-19Disease OutbreaksHumansIranLongitudinal StudiesPandemicsSearch Engine

Identifiers

PMID35881584
PMCPMC9321440

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.