ArticleJMIR public health and surveillance2017
Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends.
Article in JMIR public health and surveillance, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 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
- Google Trends and Syphilis.The Journal of clinical and aesthetic dermatology · 2024Article
- Google trends as an early indicator of African swine fever outbreaks in Southeast Asia.Frontiers in veterinary science · 2024Article
- Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.PloS one · 2023Article
- The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data.Journal of medical Internet research · 2022Article
- Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis.JMIR dermatology · 2022Article
- United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study.JMIR public health and surveillance · 2022Article
- Epidemiologic evolution of common cutaneous infestations and arthropod bites: A Google Trends analysis.JAAD international · 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
- Biannual Differences in Interest Peaks for Web Inquiries Into Ear Pain and Ear Drops: Infodemiology Study.Journal of medical Internet research · 2021Article
- Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends.JMIR public health and surveillance · 2020Article
- Article
- Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2020Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data.JMIR mental health · 2018Article
- Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era.JMIR public health and surveillance · 2018Article
- Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview.Frontiers in public health · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn extended discussion and research has been performed in recent years using data collected through search queries submitted via the Internet. It has been shown that the overall activity on the Internet is related to the number of cases of an infectious disease outbreak.
objectiveThe aim of the study was to define a similar correlation between data from Google Trends and data collected by the official authorities of Greece and Europe by examining the development and the spread of seasonal influenza in Greece and Italy.
methodsWe used multiple regressions of the terms submitted in the Google search engine related to influenza for the period from 2011 to 2012 in Greece and Italy (sample data for 104 weeks for each country). We then used the autoregressive integrated moving average statistical model to determine the correlation between the Google search data and the real influenza cases confirmed by the aforementioned authorities. Two methods were used: (1) a flu score was created for the case of Greece and (2) comparison of data from a neighboring country of Greece, which is Italy.
resultsThe results showed that there is a significant correlation that can help the prediction of the spread and the peak of the seasonal influenza using data from Google searches. The correlation for Greece for 2011 and 2012 was .909 and .831, respectively, and correlation for Italy for 2011 and 2012 was .979 and .933, respectively. The prediction of the peak was quite precise, providing a forecast before it arrives to population.
conclusionsWe can create an Internet surveillance system based on Google searches to track influenza in Greece and Italy.
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.