ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2017
Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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28 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
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- Infodemiology of Influenza-like Illness: Utilizing Google Trends' Big Data for Epidemic Surveillance.Journal of clinical medicine · 2024Article
- 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
- Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis.JMIR dermatology · 2022Article
- Excess Google Searches for Child Abuse and Intimate Partner Violence During the COVID-19 Pandemic: Infoveillance Approach.Journal of medical Internet research · 2022Article
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- Epidemiologic evolution of common cutaneous infestations and arthropod bites: A Google Trends analysis.JAAD international · 2021Article
- Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.PLoS medicine · 2021Article
- Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India.Disaster medicine and public health preparedness · 2021Article
- The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches.Technological forecasting and social change · 2021Article
- Google Trends correlation and sensitivity for outbreaks of dengue and yellow fever in the state of São Paulo.Einstein (Sao Paulo, Brazil) · 2021Article
- Leveraging Internet Search Data to Improve the Prediction and Prevention of Noncommunicable Diseases: Retrospective Observational Study.Journal of medical Internet research · 2020Observational
- Google Health Trends performance reflecting dengue incidence for the Brazilian states.BMC infectious diseases · 2020Article
- Using Google Trends to Predict Pediatric Respiratory Syncytial Virus Encounters at a Major Health Care System.Journal of medical systems · 2020Article
- Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models.Statistical methods in medical research · 2019Article
- Association of sociodemographic factors and internet query data with pertussis infections in Shandong, China.Epidemiology and infection · 2019Article
- Reappraising the utility of Google Flu Trends.PLoS computational biology · 2019Article
- Improved forecasts of influenza-associated hospitalization rates with Google Search Trends.Journal of the Royal Society, Interface · 2019Article
- Social Media Surveillance for Outbreak Projection via Transmission Models: Longitudinal Observational Study.JMIR public health and surveillance · 2019Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundLatin America has a substantial burden of influenza and rising Internet access and could benefit from real-time influenza epidemic prediction web tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy in most Latin American countries or in any low- to middle-income country. Our aim was to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay.
methodsWeekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011-December 2014. Concurrent weekly Google-predicted influenza activity in the same countries was abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country.
resultsThere were frequent GFT prediction errors, with correlation ranging from r = -0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow by Uruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific for influenza virus activity.
conclusionsWe note substantial inaccuracies with GFT-predicted influenza activity compared with FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future Internet-based biosurveillance tools.
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