Google trends: a web-based tool for real-time surveillance of disease outbreaks.

By H. Carneiro, E. Mylonakis. Clinical Infectious Diseases, 2009

815 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/78aa4d8bf8d135ea90a338f9940aec0ac26a93fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

Citations in the corpus

Online Product Opinions: Incidence, Evaluation, and Evolution (2012, background): “Furthermore, they exhibit bandwagon effects (2=.0749) by adjusting their posted evaluations upward when previous ratings have been more positive.”
Forecasting Chinese tourist volume with search engine data (2015, background): “…such as search engine query volumes, amount and types of tweets, website traffic, and social media posts to forecast contagious disease outbreaks (Carneiro & Mylonakis, 2009), consumer consumption (Vosen & Schmidt, 2011), popularity of songs and movies (Goel, Hofman, Lahaie, Pennock, & Watts,…”
The Pros and Cons of Sick Pay Schemes: Testing for Contagious Presenteeism and Noncontagious Absenteeism Behavior (2016, methodology): “Google search queries can be used via an algorithm to mimic actual ILI rates very accurately (Carneiro and Mylonakis 2009; Ginsberg et al. 2009).”
Google Trends in Infodemiology and Infoveillance: Methodology Framework (2019, result): “Regardless, Google Flu Trends has been accurate in the past in predicting the spread of flu, as suggested by several studies and reports [58-60].”
Social Network Sensors for Early Detection of Contagious Outbreaks (2010, positive): “[1] A proposal to use Google Trends to monitor online searches for information about the flu suggests that this approach could offer a better indicator, providing evidence of an outbreak at least a week before published CDC reports.[2,3] Another innovative proposal involved the use of a prediction m”
Information Retrieval: A Biomedical and Health Perspective (2020, result): “Begley and Ellis found only 11–25% of published preclinical studies were reproducible [285], with similar results obtained by Prinz et al.”
Towards a genomics-informed, real-time, global pathogen surveillance system (2017, methodology): “Increasingly, digital streams are being used as an input to these systems, be they participatory epidemiology projects such as Flu Near You 80 , the automated analysis of trending words or phrases on social media sites, such as Twitter 81,82 , or Internet search queries [83] [84] [85] .”
Statistically Significant Detection of Linguistic Change (2014, methodology): “Frequency based methods are therefore quite popular, and include tools like Google Trends and Google Books Ngram Corpus, both of which are used in research to predict economical and public health changes [8, 9].”
You Are What You Tweet: Analyzing Twitter for Public Health (2011, methodology): “Figure 2: Influenza rate from August 2009 to October 2010 as measured by CDC FluView (measured as percentage of specimen's that tested positive for influenza) and ATAM+'s " flu " ailment (the normalized probability of the " flu " ailment given the week): correlation coefficient of 0.958.”
From Predictive to Prescriptive Analytics (2014, background): “ that predict consumer demand on Black Friday based on cell-phone location data [28] and applications in public health that track latent processes of disease spread based on online web-search queries [11]. Such queries have also been used to describe consumer behavior, most notably in [13] and [19],”
Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review (2018, neutral): “Multimedia Appendix 1 consists of the first classification of the selected studies [27,39-57,59-93,96-144]; there are 104 in total, as the studies of Kohler et al [145], Orellano et al [146], Cjuno et al [147], Tejada-Llacsa [148], and Yang et al [149] are written in German, Spanish, or Chinese, and”