ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2012
Google Flu Trends: correlation with emergency department influenza rates and crowding metrics.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Forecasting influenza activity based on internet retrieval behavior and meteorological data in four megacities of China.BMC infectious diseases · 2026Article
- Paediatric invasive group A streptococcal infections and associations with viral infections in 15 European countries after lifting non-pharmaceutical interventions against SARS-CoV-2: an interrupted time-series analysis.The Lancet regional health. Europe · 2025Article
- Evaluating Google Trends as a proxy for symptom incidence: insights from the winter COVID-19 infection study in England 2023/24.Epidemiology and infection · 2025Article
- Estimating national and state-level suicide deaths using a novel online symptom search data source.Journal of affective disorders · 2023Article
- Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study.Tropical medicine and infectious disease · 2023Article
- Influenza surveillance with Baidu index and attention-based long short-term memory model.PloS one · 2023Article
- How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state.Hygiene and environmental health advances · 2022Article
- Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model Development and Performance Evaluation.JMIR medical informatics · 2022Article
- Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018.Human vaccines & immunotherapeutics · 2022Article
- COVID-19 and digital epidemiology.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2022Article
- Racial/ethnic disparities in COVID-19 disease burden & mortality among emergency department patients in a safety net health system.The American journal of emergency medicine · 2021Article
- Public Interest in Immunity and the Justification for Intervention in the Early Stages of the COVID-19 Pandemic: Analysis of Google Trends Data.Journal of medical Internet research · 2021Article
- The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches.Technological forecasting and social change · 2021Article
- Global Dieting Trends and Seasonality: Social Big-Data Analysis May Be a Useful Tool.Nutrients · 2021Article
- The predictive skill of convolutional neural networks models for disease forecasting.PloS one · 2021Article
- Front-Line Emergency Department Clinician Acceptability and Use of a Prototype Real-Time Cloud-Based Influenza Surveillance System.Frontiers in public health · 2021Article
- Article
- [Medical applications of social networks. Specific aspects of the COVID-19 pandemic].Medicine · 2020Article
- Tracking COVID-19 using taste and smell loss Google searches is not a reliable strategy.Scientific reports · 2020Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundGoogle Flu Trends (GFT) is a novel Internet-based influenza surveillance system that uses search engine query data to estimate influenza activity and is available in near real time. This study assesses the temporal correlation of city GFT data to cases of influenza and standard crowding indices from an inner-city emergency department (ED).
methodsThis study was performed during a 21-month period (from January 2009 through October 2010) at an urban academic hospital with physically and administratively separate adult and pediatric EDs. We collected weekly data from GFT for Baltimore, Maryland; ED Centers for Disease Control and Prevention-reported standardized influenzalike illness (ILI) data; laboratory-confirmed influenza data; and ED crowding indices (patient volume, number of patients who left without being seen, waiting room time, and length of stay for admitted and discharged patients). Pediatric and adult data were analyzed separately using cross-correlation with GFT.
resultsGFT correlated with both number of positive influenza test results (adult ED, r = 0.876; pediatric ED, r = 0.718) and number of ED patients presenting with ILI (adult ED, r = 0.885; pediatric ED, r = 0.652). Pediatric but not adult crowding measures, such as total ED volume (r = 0.649) and leaving without being seen (r = 0.641), also had good correlation with GFT. Adult crowding measures for low-acuity patients, such as waiting room time (r = 0.421) and length of stay for discharged patients (r = 0.548), had moderate correlation with GFT.
conclusionsCity-level GFT shows strong correlation with influenza cases and ED ILI visits, validating its use as an ED surveillance tool. GFT correlated with several pediatric ED crowding measures and those for low-acuity adult patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.