Psoriasis Google Trends.
Ontology highlight
ABSTRACT:
SUBMITTER: Garcia-Souto F
PROVIDER: S-EPMC10501518 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
Psoriasis Google Trends.
Garcia-Souto Fernando F Pereyra-Rodriguez Jose Juan JJ
JMIR dermatology 20210608 1
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Project description:Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The diversification is based on an idea that popularity of a stock measured by search queries is correlated with the stock riskiness. We penalize the popular stocks by assigning them lower portfolio weights and we bring forward the less popular, or peripheral, stocks to decrease the total riskiness of the portfolio. Our results indicate that such strategy dominates both the benchmark index and the uniformly weighted portfolio both in-sample and out-of-sample.
| S-EPMC3776958 | biostudies-other
Project description:BackgroundSalmonella infection (salmonellosis) is a common infectious disease leading to gastroenteritis, dehydration, uveitis, etc. Internet search is a new method to monitor the outbreak of infectious disease. An internet-based surveillance system using internet data is logistically advantageous and economical to show term-related diseases. In this study, we tried to determine the relationship between salmonellosis and Google Trends in the USA from January 2004 to December 2017.MethodsWe downloaded the reported salmonellosis in the USA from the National Outbreak Reporting System (NORS) from January 2004 to December 2017. Additionally, we downloaded the Google search terms related to salmonellosis from Google Trends in the same period. Cross-correlation analysis and multiple regression analysis were conducted.ResultsThe results showed that 6 Google Trends search terms appeared earlier than reported salmonellosis, 26 Google Trends search terms coincided with salmonellosis, and 16 Google Trends search terms appeared after salmonellosis were reported. When the search terms preceded outbreaks, "foods" (t = 2.927, P = 0.004) was a predictor of salmonellosis. When the search terms coincided with outbreaks, "hotel" (t = 1.854, P = 0.066), "poor sanitation" (t = 2.895, P = 0.004), "blueberries" (t = 2.441, P = 0.016), and "hypovolemic shock" (t = 2.001, P = 0.047) were predictors of salmonellosis. When the search terms appeared after outbreaks, "ice cream" (t = 3.077, P = 0.002) was the predictor of salmonellosis. Finally, we identified the most important indicators of Google Trends search terms, including "hotel" (t = 1.854, P = 0.066), "poor sanitation" (t = 2.895, P = 0.004), "blueberries" (t = 2.441, P = 0.016), and "hypovolemic shock" (t = 2.001, P = 0.047). In the future, the increased search activities of these terms might indicate the salmonellosis.ConclusionWe evaluated the related Google Trends search terms with salmonellosis and identified the most important predictors of salmonellosis outbreak.
| S-EPMC8379030 | biostudies-literature
Project description:Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal "loss of smell," "loss of taste," "fever" (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling.
| S-EPMC9520600 | biostudies-literature
Project description: Not available
| S-EPMC2815981 | biostudies-literature
Project description:Data on the number of people who have committed suicide tends to be reported with a substantial time lag of around two years. We examine whether online activity measured by Google searches can help us improve estimates of the number of suicide occurrences in England before official figures are released. Specifically, we analyse how data on the number of Google searches for the terms 'depression' and 'suicide' relate to the number of suicides between 2004 and 2013. We find that estimates drawing on Google data are significantly better than estimates using previous suicide data alone. We show that a greater number of searches for the term 'depression' is related to fewer suicides, whereas a greater number of searches for the term 'suicide' is related to more suicides. Data on suicide related search behaviour can be used to improve current estimates of the number of suicide occurrences.Electronic supplementary materialThe online version of this article (doi:10.1140/epjds/s13688-016-0094-0) contains supplementary material.
| S-EPMC7175644 | biostudies-literature
Project description:Estimation of influenza-like illness (ILI) using search trends activity was intended to supplement traditional surveillance systems, and was a motivation behind the development of Google Flu Trends (GFT). However, several studies have previously reported large errors in GFT estimates of ILI in the US. Following recent release of time-stamped surveillance data, which better reflects real-time operational scenarios, we reanalyzed GFT errors. Using three data sources-GFT: an archive of weekly ILI estimates from Google Flu Trends; ILIf: fully-observed ILI rates from ILINet; and, ILIp: ILI rates available in real-time based on partial reporting-five influenza seasons were analyzed and mean square errors (MSE) of GFT and ILIp as estimates of ILIf were computed. To correct GFT errors, a random forest regression model was built with ILI and GFT rates from the previous three weeks as predictors. An overall reduction in error of 44% was observed and the errors of the corrected GFT are lower than those of ILIp. An 80% reduction in error during 2012/13, when GFT had large errors, shows that extreme failures of GFT could have been avoided. Using autoregressive integrated moving average (ARIMA) models, one- to four-week ahead forecasts were generated with two separate data streams: ILIp alone, and with both ILIp and corrected GFT. At all forecast targets and seasons, and for all but two regions, inclusion of GFT lowered MSE. Results from two alternative error measures, mean absolute error and mean absolute proportional error, were largely consistent with results from MSE. Taken together these findings provide an error profile of GFT in the US, establish strong evidence for the adoption of search trends based 'nowcasts' in influenza forecast systems, and encourage reevaluation of the utility of this data source in diverse domains.
| S-EPMC6693776 | biostudies-literature
Project description:IntroductionAccording to our clinical experience, cellulitis is common in summer; however, very few studies have mentioned this trend.MethodsUsing Google Trends, we analyzed the monthly data of Google searches for "cellulitis" from 31 countries on 6 continents.ResultsSeasonality explained 34%-92% of the variability in search volume, with peaks occurring in summer months.ConclusionThe analyses offered new insights into the epidemiology of cellulitis on national and international scales. Clinical data are needed to validate the Internet search data.
| S-EPMC5939915 | biostudies-literature
Project description:BackgroundIncreased video-chatting, stimulated by the COVID-19 pandemic, has been correlated with increased appearance concerns. Initial lockdown restrictions correlated with a decrease in aesthetic/cosmetic plastic surgery case volumes.ObjectivesThe authors aimed to delineate public interest in aesthetic procedures surrounding the COVID-19 pandemic via Google Trends. They hypothesized that because of the pandemic, public interest in plastic surgery procedures increased, especially localized above the shoulder.MethodsTrends in the United States for given search terms and volumes were gathered via Google Trends between January 2015 and March 2021. The search volumes were normalized, and a bivariate regression analysis of panel data was then applied to the aggregate trendlines to determine if a statistically significant change in search volume occurred following the stay-at-home orders.ResultsThe following search terms had statistically significant (P < 0.000) increases in search volumes after February 2020: blepharoplasty, Botox, brachioplasty, breast implant removal, breast reduction, brow lift, buccal fat removal, hair transplantation, lip augmentation, mentoplasty, otoplasty, platysmaplasty, rhinoplasty, and thighplasty. Chi-squared analysis demonstrated a statistically significant association (chi-squared = 4.812, P = 0.028) between increases in search volume and above-the-shoulder procedures.ConclusionsPublic interest in above-the-shoulder surgical procedures statistically significantly increased following February 2020 compared with below-the-shoulder procedures. Continued examination of specific procedure trends and determining correlations with more accurate procedural datasets will provide increased insight into consumers' mindsets and to what extent video conferencing plays a role in the public's interest in pursuing aesthetic surgery.
| S-EPMC8513406 | biostudies-literature