Emerging application of Google Trends searches on “conjunctivitis” for tracing the course of COVID-19 pandemic

Pellegrini, Marco; Ferrucci, Edoardo; Guaraldi, Fabio; Bernabei, Federico; Scorcia, Vincenzo; Giannaccare, Giuseppe

doi:10.1177/11206721211042551

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…Most of the studies (83.3%) included in this review showed positive results of the possible use of GT for forecasting COVID-19 outbreaks (Tables 1 and 2). Most of the publications with positive results were performed in Western countries-mostly in Europe [8, (55.5%), less in the USA [5,7,8,13,14,17,20,22,23,25,27,30,31,34,[36][37][38][42][43][44][45][46][47][48][49][50][51][52] (51.9%), Australia [8,14,22,23,25,30,34,38] (14.8%), and Canada [22,25,30,34,38,48] (11.1%). The rest (50%) of the studies were performed in the Middle East [8,13,18,22,…”

Section: Resultsmentioning

confidence: 99%

“…Badell-Grau, Cuff, Kelly, Waller-Evans, Lloyd-Evans (2020) [14] Strong correlations found between COVID- [47] The dynamics of the correlations found between GT data COVID-19 cases and deaths suggest that it would be possible to make predictions of COVID-19 cases and mortality rates up to 3 weeks in advance. Pellegrini, Ferrucci, Guaraldi, Bernabei, Scorcia, Giannaccare (2021) [17] GT data on conjunctivitis reveals significant correlations with COVID-19 new cases with a lag of 14-18 days.…”

Section: Esmentioning

confidence: 95%

“…Most of GT COVID-19 related analyses were performed in English-speaking countries: mostly the USA [5,7,8,13,14,17,20,22,23,25,27,30,31,34,[36][37][38][42][43][44][45][46][47][48][49][50][51][52] (51.9%), as well as the United Kingdom [8,13,14,17,18,20,[22][23][24][25][26][30][31][32]34,37,38,61] (33.3%), Australia [8,14,22,23,25,…”

Section: Differences Between Countriesmentioning

confidence: 99%

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

Saegner

Austys

2022

IJERPH

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The probability of future Coronavirus Disease (COVID)-19 waves remains high, thus COVID-19 surveillance and forecasting remains important. Online search engines harvest vast amounts of data from the general population in real time and make these data publicly accessible via such tools as Google Trends (GT). Therefore, the aim of this study was to review the literature about possible use of GT for COVID-19 surveillance and prediction of its outbreaks. In November 2021, we collected and reviewed articles about the possible use of GT for COVID-19 surveillance. We resulted in 54 publications that were used in this review. The majority of the studies (83.3%) included in this review showed positive results of the possible use of GT for forecasting COVID-19 outbreaks. Most of the studies were performed in English-speaking countries (61.1%). The most frequently used keyword was “coronavirus” (53.7%), followed by “COVID-19” (31.5%) and “COVID” (20.4%). Many authors have made analyses in multiple countries (46.3%) and obtained the same results for the majority of them, thus showing the robustness of the chosen methods. Various methods including long short-term memory (3.7%), random forest regression (3.7%), Adaboost algorithm (1.9%), autoregressive integrated moving average, neural network autoregression (1.9%), and vector error correction modeling (1.9%) were used for the analysis. It was seen that most of the publications with positive results (72.2%) were using data from the first wave of the COVID-19 pandemic. Later, the search volumes reduced even though the incidence peaked. In most countries, the use of GT data showed to be beneficial for forecasting and surveillance of COVID-19 spread.

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Section: Resultsmentioning

confidence: 99%

Section: Esmentioning

confidence: 95%

Section: Differences Between Countriesmentioning

confidence: 99%

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

Saegner

Austys

2022

IJERPH

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“…Google search interest remains strongly correlated with both reported COVID-19 cases and excess mortality, with search interest in search terms such as "loss of smell", "loss of taste", and "COVID-19 symptoms" having the strongest correlation with both COVID-19 cases and excess mortality. We rely on reported COVID-19 cases for our main analysis for two reasons: first, doing so follows similar studies that test the correlation of Google search interest with report COVID-19 cases 13,14,[32][33][34] ; and second, reported COVID-19 cases are available at a daily level (instead of monthly excess mortality data) and, as of this writing, are available for a longer time period; excess mortality data from the WHO goes through 2021, while our analysis of COVID-19 cases extends through 2022 35 .…”

Section: Google Search Interest Correlating With and Preceding Covid-...mentioning

confidence: 99%

“…For each country and keyword, we compute the correlation coefficient between search interest and cases to understand which search terms tend to see the highest correlations across countries. In addition, following 8,13,32,33,[49][50][51][52][53] , we use a lag correlation analysis to understand whether search interest correlates more strongly with cases when using a lagged value of cases. We compute the correlation between search interest and cases by using lead and lag values of cases up to 21 days; the 21 day lead/lag follows from 50 .…”

Section: Google Search Interest Correlating and Anticipating Covid-19...mentioning

confidence: 99%

The Evolution of the COVID-19 Pandemic Through the Lens of Google Searches

Marty¹,

Maqueda²,

Khan³

et al. 2022

Preprint

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Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Relying on Google search interest data across 207 countries and territories, we demonstrate the capacity of publicly-available, real-time data to anticipate COVID-19 cases; evaluate the economic, mental health, and social impacts of containment policies; and identify demand for (mis)information about COVID-19 vaccines. We show that: (1) search interest in COVID-specific symptoms can anticipate rising COVID-19 cases across both high- and low-income settings; (2) countries with more restrictive containment policies experienced larger socio-economic externalities; in addition, lower-income countries experienced less searches for unemployment, but more pronounced mental health externalities; and (3) high vaccination rates are associated with strong demand for information about vaccine appointments and side effects; in some settings, high interest in misinformation search terms is associated with low vaccination rates. Overall, the results demonstrate that real-time search interest data can be a valuable tool for both high- and low-income countries to inform policies across multiple stages of the pandemic.

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The evolution of the COVID-19 pandemic through the lens of google searches

Marty,

Ramos-Maqueda,

Khan

et al. 2023

Sci Rep

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Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Using data from 221 countries and territories, we demonstrate the capacity of Google search data to anticipate reported COVID-19 cases and understand how containment policies are associated with changes in socioeconomic indicators. First, search interest in COVID-specific symptoms such as “loss of smell” strongly correlated with cases initially, but the association diminished as COVID-19 evolved; general terms such as “COVID symptoms” remained strongly associated with cases. Moreover, trends in search interest preceded trends in reported cases, particularly in the first year of the pandemic. Second, countries with more restrictive containment policies experienced greater search interest in unemployment and mental health terms after policies were implemented, indicating socio-economic externalities. Higher-income countries experienced a larger increase in searches related to unemployment and a larger reduction in relationship and family planning keywords relative to lower-income countries. The results demonstrate that real-time search interest can be a valuable tool to inform policies across multiple stages of the pandemic.

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Emerging application of Google Trends searches on “conjunctivitis” for tracing the course of COVID-19 pandemic

Cited by 4 publications

References 26 publications

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

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

The Evolution of the COVID-19 Pandemic Through the Lens of Google Searches

The evolution of the COVID-19 pandemic through the lens of google searches

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