Seasonality of cellulitis: evidence from Google Trends.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang X
PROVIDER: S-EPMC5939915 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The COVID-19 pandemic and government intervention such as lockdowns may severely affect people's mental health. While lockdowns can help to contain the spread of the virus, they may result in substantial damage to population well-being. We use Google Trends data to test whether COVID-19 and the associated lockdowns implemented in Europe and America led to changes in well-being related topic search-terms. Using difference-in-differences and a regression discontinuity design, we find a substantial increase in the search intensity for boredom in Europe and the US. We also found a significant increase in searches for loneliness, worry and sadness, while searches for stress, suicide and divorce on the contrary fell. Our results suggest that people's mental health may have been severely affected by the pandemic and lockdown.
| S-EPMC7703221 | biostudies-literature
Project description:Today´s connected world allows people to gather information in shorter intervals than ever before, widely monitored by massive online data sources. As a dramatic economic event, recent financial crisis increased public interest for large companies considerably. In this paper, we exploit this change in information gathering behavior by utilizing Google query volumes as a "bad news" indicator for each corporation listed in the Standard and Poor´s 100 index. Our results provide not only an investment strategy that gains particularly in times of financial turmoil and extensive losses by other market participants, but reveal new sectoral patterns between mass online behavior and (bearish) stock market movements. Based on collective attention shifts in search queries for individual companies, hence, these findings can help to identify early warning signs of financial systemic risk. However, our disaggregated data also illustrate the need for further efforts to understand the influence of collective attention shifts on financial behavior in times of regular market activities with less tremendous changes in search volumes.
| S-EPMC4530949 | biostudies-literature
Project description:Seasonal and human physiological changes are important factors in the development of many diseases. But, the study of genuine seasonal impact on these diseases is difficult to measure due to many other environment and lifestyle factors which directly affect these diseases. However, several clinical studies have been conducted in different parts of the world, and it has clearly indicated that certain groups of population are highly subjected to seasonal changes, and their maladaptation can possibly lead to several disorders/diseases. Thus, it is crucial to study the significant seasonal sensitive diseases spread across the human population. To narrow down these disorders/diseases, the study hypothesized that high altitude (HA) associated diseases and disorders are of the strong variants of seasonal physiologic changes. It is because, HA is the only geographical condition for which humans can develop very efficient physiological adaptation mechanism called acclimatization. To study this hypothesis, PubMed was used to collect the HA associated symptoms and disorders. Disease Ontology based semantic similarity network (DSN) and disease-drug networks were constructed to narrow down the benchmark diseases and disorders of HA. The DSN which was further subjected to different community structure analysis uncovered the highly associated or possible comorbid diseases of HA. The predicted 12 lifestyle diseases were assumed to be "seasonal (sensitive) comorbid lifestyle diseases (SCLD)". A time series analyses on Google Search data of the world from 2004-2016 was conducted to investigate whether the 12 lifestyle diseases have seasonal patterns. Because, the trends were sensitive to the term used as benchmark; the temporal relationships among the 12 disease search volumes and their temporal sequences similarity by dynamic time warping analyses was used to predict the comorbid diseases. Among the 12 lifestyle diseases, the study provides an indirect evidence in the existence of severe seasonal comorbidity among hypertension, obesity, asthma and fibrosis diseases, which is widespread in the world population. Thus, the present study has successfully addressed this issue by predicting the SCLD, and indirectly verified them among the world population using Google Search Trend. Furthermore, based on the SCLD seasonal trend, the study also classified them as severe, moderate, and mild. Interestingly, seasonal trends of the severe seasonal comorbid diseases displayed an inverse pattern between USA (Northern hemisphere) and New Zealand (Southern hemisphere). Further, knowledge in the so called "seasonal sensitive populations" physiological response to seasonal triggers such as winter, summer, spring, and autumn become crucial to modulate disease incidence, disease course, or clinical prevention.
| S-EPMC6291106 | biostudies-literature
Project description:BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has posed stress on the health and well-being of both Chinese people and the public worldwide. Global public interest in this new issue largely reflects people's attention to COVID-19 and their willingness to take precautionary actions. This study aimed to examine global public awareness of COVID-19 using Google Trends.MethodsUsing Google Trends, we retrieved public query data for terms of "2019-nCoV + SARS-CoV-2 + novel coronavirus?+?new coronavirus + COVID-19 + Corona Virus Disease 2019" between the 31st December 2019 and the 24th February 2020 in six major English-speaking countries, including the USA, the UK, Canada, Ireland, Australia, and New Zealand. Dynamic series analysis demonstrates the overall change trend of relative search volume (RSV) for the topic on COVID-19. We compared the top-ranking related queries and sub-regions distribution of RSV about COVID-19 across different countries. The correlation between daily search volumes on the topic related to COVID-19 and the daily number of people infected with SARS-CoV-2 was analyzed.ResultsThe overall search trend of RSV regarding COVID-19 increased during the early period of observing time and reached the first apex on 31st January 2020. A shorter response time and a longer duration of public attention to COVID-19 was observed in public from the USA, the UK, Australia, and Canada, than that in Ireland and New Zealand. A slightly positive correlation between daily RSV about COVID-19 and the daily number of confirmed cases was observed (P?<?0.05). People across countries presented a various interest to the RSV on COVID-19, and public awareness of COVID-19 was different in various sub-regions within countries.ConclusionsThe results suggest that public response time to COVID-19 was different across countries, and the overall duration of public attention was short. The current study reminds us that governments should strengthen the publicity of COVID-19 nationally, strengthen the public's vigilance and sensitivity to COVID-19, inform public the importance of protecting themselves with enough precautionary measures, and finally control the spread of COVID-19 globally.
| S-EPMC7182392 | 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 Material:The online version of this article (doi:10.1140/epjds/s13688-016-0094-0) contains supplementary material.
| S-EPMC7175644 | 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.ObjectivesWe aim to delineate public interest in aesthetic procedures surrounding the COVID-19 pandemic via Google Trends (GT). We hypothesize that because of the pandemic, public interest in plastic surgery procedures increased, especially localized above the shoulder.MethodsTrends in the US for given search terms and volumes were gathered via Google Trends between January 2015 to 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 showed a statistically significant increase following February 2020 compared to below the shoulder procedures. Continued examination of specific procedure trends, as well as determining correlations with more accurate procedural datasets, will provide increased insight into consumers' mindsets and to what extent video conferencing plays on the public's interest in pursuing aesthetic surgery.
| S-EPMC8513406 | biostudies-literature
Project description: Not available
| S-EPMC2815981 | 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: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