ArticleScientific reports2016
Disease surveillance based on Internet-based linear models: an Australian case study of previously unmodeled infection diseases.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic.Journal of epidemiology and global health · 2024Pooled it
- Epidemiological models for predicting Ross River virus in Australia: A systematic review.PLoS neglected tropical diseases · 2020Pooled it
- Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Advances in Animal Disease Surveillance and Information Systems and Their Role in Disease Control and Prevention: Implications in Ethiopia.Veterinary medicine and science · 2025Review
- 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 systems using traditional and alternative sources of data: A scoping review.Influenza and other respiratory viruses · 2022Article
- Participatory COVID-19 Surveillance Tool in Rural Appalachia : Real-Time Disease Monitoring and Regional Response.Public health reports (Washington, D.C. : 1974) · 2021Article
- Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations.Journal of medical Internet research · 2020Article
- Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.International journal of environmental research and public health · 2020Article
- Google Health Trends performance reflecting dengue incidence for the Brazilian states.BMC infectious diseases · 2020Article
- Predicting Infectious Disease Using Deep Learning and Big Data.International journal of environmental research and public health · 2018Article
- National Communicable Disease Surveillance System: A review on Information and Organizational Structures in Developed Countries.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2017Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective disease surveillance is critical to the functioning of health systems. Traditional approaches are, however, limited in their ability to deliver timely information. Internet-based surveillance systems are a promising approach that may circumvent many of the limitations of traditional health surveillance systems and provide more intelligence on cases of infection, including cases from those that do not use the healthcare system. Infectious disease surveillance systems built on Internet search metrics have been shown to produce accurate estimates of disease weeks before traditional systems and are an economically attractive approach to surveillance; they are, however, also prone to error under certain circumstances. This study sought to explore previously unmodeled diseases by investigating the link between Google Trends search metrics and Australian weekly notification data. We propose using four alternative disease modelling strategies based on linear models that studied the length of the training period used for model construction, determined the most appropriate lag for search metrics, used wavelet transformation for denoising data and enabled the identification of key search queries for each disease. Out of the twenty-four diseases assessed with Australian data, our nowcasting results highlighted promise for two diseases of international concern, Ross River virus and pneumococcal disease.
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