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

Saegner, Tobias; Austys, Donatas

doi:10.3390/ijerph191912394

Cited by 16 publications

(9 citation statements)

References 66 publications

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“…Diverse analytic methodologies, language restrictions, and testing policies preclude a unified evaluation. However, the findings were supportive across various geographic regions, and the majority of studies (83%) incorporating web search data concur on the applicability of search engines when studying the COVID-19 pandemic [ 9 ]. Despite the limitations associated with big data [ 10 , 11 ], in situations of profound population concern, such as the COVID-19 pandemic, examining online browsing behavior may help understand disease trajectory.…”

Section: Introductionmentioning

confidence: 94%

“…Specifically, web search engines and social media activity can serve as early warning signals for infectious disease outbreaks, case forecasting, and hot-spot identification and complement traditional surveillance methods. Google Trends data have been used for correlation and forecasting studies in influenza outbreaks [ 5 ], Zika virus [ 6 ], Middle East respiratory syndrome (MERS) [ 7 ], measles [ 8 ], and, recently, COVID-19 [ 9 ]. Diverse analytic methodologies, language restrictions, and testing policies preclude a unified evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data

Ziakas,

Mylonakis

2024

PLOS Digit Health

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Google Trends provides spatiotemporal data for user-specific terms scaled from less than 1 (lowest relative popularity) to 100 (highest relative popularity) as a proxy for the public interest. Here we use US state-level data for COVID-19 to examine popularity trends during the pandemic evolution. We used "coronavirus" and "covid" search terms and set the period up from January 1st, 2020, to November 12, 2022. We measured the agreement on web rankings between states using the nonparametric Kendall’s W (0 for no concordance to 1 for perfect agreement). We compiled state-level weekly data on COVID-19 incidence and mortality and scaled state curves from 0 to 100 through a min-max normalization process. We used a dynamic time-warping algorithm to calculate similarities between the popularity, mortality, and incidence of COVID-19. The methodology is a pattern recognition process between time series by distance optimization. The similarity was mapped from 0 to 1, with 1 indicating perfect similarity and 0 indicating no similarity. The peak in popularity was in March 2020, succeeded by a decline and a prolonged period of fluctuation around 20%. Public interest rose briefly at the end of 2021, to fall to a low activity of around 10%. This pattern was remarkably consistent across states (Kendal’s W 0.94, p < 0.001). Web search trends were an impression of contagion growth: Overall, popularity-mortality trajectories yielded higher similarity indices (median 0.78; interquartile range 0.75–0.82) compared to popularity-incidence trajectories (median 0.74; interquartile range 0.72–0.76, Wilcoxon’s exact p<0.001). The popularity-mortality trajectories had a very strong similarity (>0.80) in 19/51 (37%) regions, as opposed to only 4/51 (8%) for popularity-incidence trajectories. State-level data show a fading public concern about COVID-19, and web-search popularity patterns may reflect the COVID-19 trajectory in terms of cases and mortality.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data

Ziakas,

Mylonakis

2024

PLOS Digit Health

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“…For example, when many people search for the term ' u', this indicates an outbreak is about to occur. Recently, GT has been used to forecast and monitor the spread of Covid-19 (Saegner & Austys, 2022). Those interested in the opportunities and limitations of GT in health and health policy research are referred to Arora, McKee, and Stuckler (2019).…”

Section: Applications In Academic Researchmentioning

confidence: 99%

15 years of research on Google Trends: A bibliometric review and future research directions

Madsen

Silva

Sohail

2023

Preprint

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Google Trends is a free analytic tool for measuring and monitoring web search data. Since its introduction in 2006, it has been widely used in research in a variety of academic fields. In this study, we conducted a bibliometric study of the first 15 years of Google Trends research (2007–2022). We extracted bibliometric data from the Scopus database which were then analyzed quantitatively using the Bibliometrix software package. In the paper, two types of analyses are carried out: 1) a citation-based analysis that focuses on the overall publication trends and the identification of the most prolific authors, documents, journals, and countries, and 2) a mapping of themes in GT research and how these have evolved. Overall, the results show that the research on Google Trends has grown considerably in recent years, particularly since the outbreak of the Covid-19 pandemic. Research on GT has also become more diverse in terms of themes, with finance, Covid-19 and other health-related topics being the new dominant areas of research. On the basis of the findings, several areas for future research are outlined.

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“…Through multivariate time-series analysis, the transfer function models forecast the development trend of COVID-19, which is of great importance in pandemic prevention and control. A few researchers have used Google Trends data to forecast trends in the development of diseases, including COVID-19 (13)(14)(15). Mavragani and Gkillas (16) applied regression analysis to Google search data on COVID-19 in the United States and found a statistically significant correlation between Google Trends and COVID-19 data.…”

Section: Summary Of the Study And Comparison With Contemporaneous Stu...mentioning

confidence: 99%

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss

Chen

et al. 2022

Front. Public Health

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AimTo explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss.MethodsData on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords “loss of smell” and “loss of taste” were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases.ResultsFrom 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19.ConclusionGoogle Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1–3 weeks in advance.

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

Cited by 16 publications

References 66 publications

Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data

Public interest trends for COVID-19 and pandemic trajectory: A time-series analysis of US state-level data

15 years of research on Google Trends: A bibliometric review and future research directions

Construction and validation of a COVID-19 pandemic trend forecast model based on Google Trends data for smell and taste loss

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