ArticleInfluenza and other respiratory viruses2016
Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.
Article in Influenza and other respiratory viruses, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials.Expert review of anti-infective therapy · 2017Pooled it
- Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.PLoS computational biology · 2026Article
- A Deep Learning Framework for Using Search Engine Data to Predict Influenza-Like Illness and Distinguish Epidemic and Nonepidemic Seasons: Multifeature Time Series Analysis.Journal of medical Internet research · 2025Article
- Forecasting severe respiratory disease hospitalizations using machine learning algorithms.BMC medical informatics and decision making · 2024Article
- Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand.PLoS computational biology · 2024Article
- Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020.Scientific reports · 2023Article
- Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study.JMIR medical informatics · 2021Article
- Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data.PLoS computational biology · 2020Article
- Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina.PloS one · 2020Article
- Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples.BMC public health · 2019Article
- Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China.EBioMedicine · 2019Article
- Development and validation of influenza forecasting for 64 temperate and tropical countries.PLoS computational biology · 2019Article
- Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.Tropical medicine and infectious disease · 2019Article
- Applying particle filtering in both aggregated and age-structured population compartmental models of pre-vaccination measles.PloS one · 2018Article
- Model selection for seasonal influenza forecasting.Infectious Disease Modelling · 2017Article
- Retrospective forecasting of the 2010-2014 Melbourne influenza seasons using multiple surveillance systems.Epidemiology and infection · 2017Article
- Zika pandemic online trends, incidence and health risk communication: a time trend study.BMJ global health · 2017Article
- Translating Big Data into Smart Data for Veterinary Epidemiology.Frontiers in veterinary science · 2017Article
- Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.Influenza and other respiratory viruses · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system.
objectivesOur aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia).
methodsWe used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006-14. RESULTS AND
conclusionsOptimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic (ROC) curves. We show that the timing of the epidemic peak can be accurately predicted 4-6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support.
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