Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis

Venkatesh, U; Gandhi, P. Aravind

doi:10.4258/hir.2020.26.3.175

Cited by 56 publications

(49 citation statements)

References 17 publications

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“…in a fixed timelapse. In this regard, the quantitative analysis of relative search volumes of pre-selected queries was used for several purposes during COVID-19 pandemic: 1) predicting COVID-19 cases ( Ahmad et al, 2020 ; Ayyoubzadeh et al, 2020 ; Jimenez et al, 2020 ; Mavragani and Gkillas, 2020 ; Sulyok et al, 2020 ; Venkatesh and Gandhi, 2020 ; Prasanth et al, 2021 ), 2) studying the web interest in COVID-19 ( Effenberger et al, 2020 ; Hu et al, 2020 ; Rovetta and Castaldo, 2020 ; Springer et al, 2020 ), 3) studying the adoption of infodemic terms and related consequences ( Cinelli et al, 2020 ; Cuan-Baltazar et al, 2020 ; Rovetta and Bhagavathula, 2020 ), 4) studying a full range of users’ psychological-emotional responses ( Husnayain et al, 2020 ; Rovetta and Castaldo, 2020 ; Zattoni et al, 2020 ; Brodeur et al, 2021 ; Zitting et al, 2021 ), 5) studying the impact of mass media and governmental policies on users’ web searches ( Rovetta and Bhagavathula, 2020 ; Sousa-Pinto et al, 2020 ; Huynh Dagher et al, 2021 ), 6) studying the economic-commercial impact ( Brodeur et al, 2021 ; Sotis, 2021 ), 7) studying the spread of COVID-19 symptoms ( Ahmad et al, 2020 ; Jimenez et al, 2020 ; Kluger and Scrivener, 2020 ; Walker et al, 2020 ), 8) studying other various web interests ( Berger et al, 2021 ; Elsaie and Youssef, 2021 ). This type of research is mainly based on the search for statistical cross-correlations between users’ web searches related to specific topics, such as symptoms, drugs, therapies, vaccines, number of infected people, number of deaths, anxiety, fear, stress, etc., and the number of disease contagions and deaths officially registered after a certain timespan.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

Rovetta¹

2021

Front. Res. Metr. Anal.

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Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity.Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected.Methods: RSVs of the query coronavirus + covid during February 1—December 4, 2020 (period 1), and February 20—May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution Xi=X(σi,x¯i) was used to represent the trend of daily variations of xij=RSVsij. When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Welch’s t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t<1.5. A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). The percentage increase Δ was used to quantify the difference between two values.Results: Google Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both periods 1 and 2) and countries worldwide (9.7% during period 1 and 10.9% during period 2). However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets (Max |Δ| = + 625% for Italian regions, and Max |Δ|= +175% for countries worldwide). Furthermore, only RSVs of countries worldwide did not exceed confidence threshold. Finally, the large amount of anomalies registered in Italian and international cities’ RSVs made these datasets unusable for any kind of statistical inference.Conclusion: In the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide. Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries’ data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected. Further research is needed to evaluate the effectiveness of this method.

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Section: Introductionmentioning

confidence: 99%

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

Rovetta¹

2021

Front. Res. Metr. Anal.

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“…This requires caution when analyzing results and making interpretations from the analyses. Therefore, it is strongly recommended that Google trends should not be used as a sole replacement for robust disease surveillance and health literacy monitoring, rather it should be a real-time supplement to the comprehensive disease monitoring [17].…”

Section: Discussionmentioning

confidence: 99%

“…However, infodemiology studies exploring the association of web queries to the COVID-19 public interest and awareness in South Asia received limited attention. So far, only one study showed promise in exploring the potential use of Google Trends in predicting the COVID-19 outbreak in India, and yet, the relationship between web queries and public health preparedness remained unclear [17]. In addition, there is no Infodemiology study till now that has explored the association of web queries to the COVID-19 public internet and awareness in other seven South Asian countries.…”

Section: Introductionmentioning

confidence: 99%

Monitoring COVID-19 related public Interest and population Health Literacy in South Asia: An Internet Search-Interest Based Model

Symum

Sagor

2020

Preprint

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Background Information epidemiology based on internet search data can be used to model COVID-19 pandemic progressions and monitor population health literacy. However, the applicability of internet searches to monitor COVID-19 infections and public health awareness in South Asian countries are unclear. Objectives To assess the association of public interest and health literacy in COVID-19 with the actual number of infected cases for countries in South Asia. Methods Google Trends data from January to March 2020 were used to correlate public interest and health literacy with official data on COVID-19 cases using the relative search volume (RSV) index. Public interest in COVID-19 was retrieved in RSV indices with the search term Coronavirus (Virus). Similarly, an OR combination of search terms hand wash, face mask, hand sanitizer, face shield, and gloves were used to retrieve RSV indices as a surrogate of population health literacy in COVID-19. Daily confirmed COVID-19 cases were obtained from the COVID-19 data repository managed by the Johns Hopkins University. Country-level time-lag correlation analyses were performed for a time lag between 30 and +30 days. Results COVID-19-related worldwide public interest reached a peak on March 16, 2020, right after the WHO announcement of coronavirus outbreak as a pandemic. COVID-19 related public interest reached the highest peak in South Asian countries a few days after each county reported 100th confirmed cases. There were significant positive correlations between COVID-19 related public interest and daily laboratory-confirmed cases in countries expect Nepal, Bhutan, and Sri Lanka. The highest public interest in South Asian Countries was on average 12 days before the local maximum of new confirmed cases. Similarly, web searches related to personal hygiene and COVID-19 preventive measures in south Asia correlated to the number of confirmed cases as well as national restriction measures. Conclusion Public interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic particularly in countries with a lack of diagnostic and surveillance capacity, and thereby distribute appropriate health information to the general public.

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“…In this regard, scientists are increasingly adopting infoveillance tools to monitoring the infodemic on websites, social media, and newspapers [5]. In particular, Google Trends-an open online infoveillance tool developed by Google TM -has been widely used by the scientific community not only as for quantifying disinformation but also to make epidemiological predictions on the spread of infectious diseases, including COVID-19 [6][7][8][9]. This type of study is based on the search for statistical cross-correlations between users' web searches related to specific diseases, such as symptoms, drugs, therapies, vaccines, number of infected people, number of deaths, etc., and the number of disease contagions and deaths officially registered after a certain timespan.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

Rovetta¹

2021

Preprint

View full text Add to dashboard Cite

BackgroundAlongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity.ObjectiveThe aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected.MethodsRSVs of the query coronavirus + covid during February 1 - December 4, 2020 (period 1), and February 20 - May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution was used to represent the trend of daily variations of xij = RSVsij. When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Student t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t < 1.5. A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). The percentage increase Δ was used to quantify the difference between two values.ResultsGoogle Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both period 1 and period 2) and countries worldwide (9.7% during period 1 and 10.9% during period 2). However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets (Max |Δ| = + 625% for Italian regions, and Max |Δ| = +175% for countries worldwide). Furthermore, only RSVs of countries worldwide did not exceed confidence threshold. Finally, the large amount of anomalies registered in Italian and international cities’ RSVs made these datasets unusable for any kind of statistical inference.ConclusionsIn the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide. Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries’ data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected.

show abstract

Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis

Cited by 56 publications

References 17 publications

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

Monitoring COVID-19 related public Interest and population Health Literacy in South Asia: An Internet Search-Interest Based Model

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

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