Google search volume predicts the emergence of COVID-19 outbreaks

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends

Introduction

Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system.

Methods

The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence.

Results

Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche.

Conclusion

Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Alternative and Adjunct Treatments for Scoliosis: A Google Trends Analysis of Public Popularity Compared With Scientific Literature

Purpose As Google searches have often been found to provide inaccurate information regarding various treatments for orthopedic conditions, it becomes important to analyze search trends to understand what treatments are most popularly considered and the quality of information available. We sought to compare the public interest in popular adjunct/alternative scoliosis treatments to the published literature on these topics and assess any temporal trends in the public interest in these treatments. Methods The study authors compiled the most common adjunct/alternative treatments for scoliosis on PubMed. Chiropractic manipulation, Schroth exercises, physical therapy, pilates, and yoga, along with "scoliosis," were each entered into Google Trends, collected from 2004 to 2021. A linear regression analysis of covariance (ANCOVA) was done to determine whether there was a linear relationship between Google Trends' popularity and PubMed publication data. The seasonal popularity of the terms was assessed using locally estimated scatterplot smoothing (LOESS) regression. Results Google Trends and publication frequency linear regression curves were different for chiropractic manipulation (p < 0.001), Schroth exercises (p < 0.001), physical therapy (p < 0.001), and pilates (p = 0.003). Chiropractic manipulation (p < 0.001), Schroth exercises (p = 0.003), and physical therapy (p < 0.001) had positive trends, and yoga (p < 0.001) had a negative trend. Chiropractic manipulation and yoga were more popular in the summer and winter months. Conclusion Google Trends can provide orthopedic surgeons and other healthcare professionals with valuable information on which treatments are gaining popularity with the public, so physicians may specifically inform themselves prior to patient encounters, leading to more productive shared decision-making.

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

Aim

To 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.

Methods

Data 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.

Results

From 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.

Conclusion

Google 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.

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

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. We collected and reviewed articles about the possible use of GT for COVID-19 surveillance published in the first 2 years of the pandemic. 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.

COVID-19 Infodemiology: Association Between Google Search and Vaccination in Malaysian Population

Background

In light of the ongoing coronavirus disease 2019 (COVID-19) pandemic, vaccination is one of the most important defensive strategies in combating the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccine hesitancy or anti-vaccination attitude has become a barrier to the nationwide vaccination program, potentially sabotaging the effectiveness of vaccination. Thus far, Google Trends (GT) has been used extensively for monitoring information-seeking behavior during the pandemic. We aimed to investigate the association between Google search, the vaccination rate, and the number of vaccinated and infected cases among the Malaysian population.

Material and method

GT’s customizable geographic and temporal filters were applied to include results for predetermined keywords from January 1, 2021, to December 31, 2021. Both Malay and English languages were used to reflect the multi-racial and multi-lingual community in Malaysia. The search volume index (SVI) derived was compared with the numbers of vaccinated and infected cases which were extracted from the open-access database (COVIDNOW in Malaysia) within the same period. Both analyses were performed independently by two authors to ensure accuracy of the data extraction process. A descriptive analysis was used to compare GT analyses and the number of daily vaccinations and positive COVID-19 cases.

Results

The information-seeking behavior in the public fluctuated from time to time. The interest surged during the initiation of vaccination program and upon the outbreak of COVID-19 in Malaysia. The surge in interest prior to the peak of vaccination rate also indicated that the public tended to get information online prior to getting the vaccines. 

Conclusion

This observational study illustrates the ability of GT to monitor the interest of vaccination among the Malaysian population during the pandemic. By monitoring the dynamic changes in Google Trends, healthcare authorities can get a glimpse of public perceptions such as attitude towards COVID-19 vaccine, hence potentially identify and stymie any dangerous online anti-vaccination rhetoric swiftly.

COVID-19 and thyroid disease: An infodemiological pilot study

BACKGROUND

Google Trends searches for symptoms and/or diseases may reflect actual disease epidemiology. Recently, Google Trends searches for coronavirus disease 2019 (COVID-19)-associated terms have been linked to the epidemiology of COVID-19. Some studies have linked COVID-19 with thyroid disease.

AIM

To assess COVID-19 cases per se vs COVID-19-associated Google Trends searches and thyroid-associated Google Trends searches.

METHODS

We collected data on worldwide weekly Google Trends searches regarding “COVID-19”, “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)”, “coronavirus”, “smell”, “taste”, “cough”, “thyroid”, “thyroiditis”, and “subacute thyroiditis” for 92 wk and worldwide weekly COVID-19 cases' statistics in the same time period. The study period was split in half (approximately corresponding to the preponderance of different SARS-COV-2 virus variants) and in each time period we performed cross-correlation analysis and mediation analysis.

RESULTS

Significant positive cross-correlation function values were noted in both time periods. More in detail, COVID-19 cases per se were found to be associated with no lag with Google Trends searches for COVID-19 symptoms in the first time period and in the second time period to lead searches for symptoms, COVID-19 terms, and thyroid terms. COVID-19 cases per se were associated with thyroid-related searches in both time periods. In the second time period, the effect of “COVID-19” searches on “thyroid’ searches was significantly mediated by COVID-19 cases (P = 0.048).

CONCLUSION

Searches for a non-specific symptom or COVID-19 search terms mostly lead Google Trends thyroid-related searches, in the second time period. This time frame/sequence particularly in the second time period (noted by the preponderance of the SARS-COV-2 delta variant) lends some credence to associations of COVID-19 cases per se with (apparent) thyroid disease (via searches for them).

The national burden of scabies in Germany: a population-based approach using Internet search engine data

PURPOSE

Scabies is a World Health Organization-defined neglected tropical disease and a growing public health issue worldwide. It is difficult to obtain reliable data on prevalence due to the lack of standardized tests. The aim of this study was to assess scabies online search behavior in Germany to identify local differences using Google search volume.

METHODS

Google Ads Keyword Planner was used to investigate the scabies-related search volume for Germany as a whole, its 16 federal states, and 15 large cities for the period from January 2016 to December 2019. The identified search terms were qualitatively categorized and critically analyzed.

RESULTS

A total of 572 keywords with an overall search volume of 11,414,180 searches regarding scabies were identified in Germany. The number of searches was higher in winter than in summer, with a national peak in March 2018. Around 30.6% of the searches regarding scabies therapy (n = 978,420) were related to home remedies. Regarding body localization, most searches focused on the whole body (n = 109,050), followed by head (n = 89,360) and the genital area (n = 28,640).

CONCLUSIONS

The analysis of Google search data provides an overview of the populations' interest regarding scabies. The analysis can detect local peaks and assess the relevance of scabies at individual localizations of the body. The study highlighted current possible shortcomings in the therapy of scabies. It also underlined the importance of improving awareness regarding scabies so that affected individuals can consult a doctor earlier for treatment.

Ups and Downs of COVID-19: Can We Predict the Future? Local Analysis with Google Trends for Forecasting the Burden of COVID-19 in Pakistan

BACKGROUND

We aim to study the utility of Google Trends search history data for demonstrating if a correlation may exist between web-based information and actual coronavirus disease 2019 (COVID-19) cases, as well as if such data can be used to forecast patterns of disease spikes.

PATIENTS & METHODS

Weekly data of COVID-19 cases in Pakistan was retrieved from online COVID-19 data banks for a period of 60 weeks. Search history related to COVID-19, coronavirus and the most common symptoms of disease was retrieved from Google Trends during the same period. Statistical analysis was performed to analyze the correlation between the two data sets. Search terms were adjusted for time-lag over weeks, to find the highest cross-correlation for each of the search terms.

RESULTS

Search terms of 'fever' and 'cough' were the most commonly searched online, followed by coronavirus and COVID. The highest peak correlations with the weekly case series, with a 1-week backlog, was noted for loss of smell and loss of taste. The combined model yielded a modest performance for forecasting positive cases. The linear regression model revealed loss of smell (adjusted R² of 0.7) with significant 1-week, 2-week and 3-week lagged time series, as the best predictor of weekly positive case counts.

CONCLUSIONS

Our local analysis of Pakistan-based data seemingly confirms that Google trends can be used as an important tool for anticipating and predicting pandemic patterns and pre-hand preparedness in such unprecedented pandemic crisis.

Italian and Japanese public attention toward balneotherapy in the COVID-19 era

Italian and Japanese public widely use balneotherapy. The population interest in balneotherapy in coronavirus disease-2019 (COVID-19) era should be investigated. Therefore, we aimed to exploit Google Trends analysis, as a measure of peoples' interest in balneotherapy, in two countries, Italy and Japan. In this infodemiology study, Google Trends was queried for the lay terms widely used by the Italian population to refer to the balneotherapy setting (terme + termale) and by the Japanese to refer to the bathing place and balneotherapy facilities ( + スパ). The internet searches in 2020 were compared to overlapping time spans in 2016-2019 and were correlated with new confirmed cases/deaths. This study demonstrated that from February 23 to June 20, 2020, and from October 4 to December 26, 2020, the internet searches of the Italian words corresponding to balneotherapy were statistically significantly decreased; however, the internet searches were not significantly different in June 21 to October 3, 2020, compared to overlapping time spans in 2016-2019 in Italy. The study also showed that from March 15 to September 5, 2020, and from November 29 to December 26, 2020, the internet searches of the Japanese words corresponding to balneotherapy were statistically significantly decreased; however, the internet searches were significantly increased in September 13 to November 7, 2020, and were not significantly different in November 8 to 28, 2020, compared to overlapping time spans in 2016-2019 in Japan. There were significant negative correlations between the relative search volume and number of new cases (rho=-0.634; p<0.001)/deaths (rho=-0.856; p<0.001) in Italy and the number of new deaths (rho=-0.348; p=0.012) in Japan. Population interest in balneotherapy has changed in the COVID-19 era both in Italy and Japan. During the early stage of pandemic (March to June), the interest was lower. After this early stage, the interest showed a recovery in both countries. In Italy, the population interest reached to its prior levels in late June through early October, with a peak in August. In Japan, the recovery exceeded the prior 4-year levels in mid-September through early November. Then, both countries demonstrated a decline in interest: began in early October in Italy and late November in Japan. This information would allow us to understand/address the population response in the pandemic in respect of the balneotherapy and would guide the preparedness of healthcare providers and planners both in this pandemic and future similar situations.

The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

This study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

Attitudes and Beliefs on Influenza Vaccination during the COVID-19 Pandemic: Results from a Representative Italian Survey

The last 2019/20 northern hemisphere influenza season overlapped with the first wave of coronavirus disease 2019 (COVID-19) pandemic. Italy was the first western country where severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread to a significant extent. In this representative cross-sectional survey, we aimed to describe some opinions and attitudes of the Italian general population towards both influenza vaccination and the COVID-19 pandemic, and to identify potential modifiers of the decision-making process regarding the uptake of the 2020/21 influenza vaccine. A total of 2543 responses were analyzed. Although most (74.8%) participants valued influenza vaccination positively and declared that it should be mandatory, some misconceptions around influenza persist. The general practitioner was the main source of trusted information on influenza vaccines, while social networks were judged to be the least reliable. Younger and less affluent individuals, subjects not vaccinated in the previous season, and those living in smaller communities showed lower odds of receiving the 2020/21 season influenza vaccination. However, the COVID-19 pandemic may have positively influenced the propensity of being vaccinated against 2020/21 seasonal influenza. In order to increase influenza vaccination coverage rates multidisciplinary targeted interventions are needed. The role of general practitioners remains crucial in increasing influenza vaccine awareness and acceptance by effective counselling.