ArticleEpidemiology and infection2017
Retrospective forecasting of the 2010-2014 Melbourne influenza seasons using multiple surveillance systems.
Article in Epidemiology and infection, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.PLoS computational biology · 2026Article
- A Framework for Evaluating the Use of Surveillance Systems for Short-Term Influenza Forecasting.Influenza and other respiratory viruses · 2025Article
- Estimating epidemic dynamics with genomic and time series data.Journal of the Royal Society, Interface · 2025Article
- Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand.Communications medicine · 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
- Accurate influenza forecasts using type-specific incidence data for small geographic units.PLoS computational biology · 2021Article
- Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel.PLoS computational biology · 2020Article
- Epidemiological data from the COVID-19 outbreak, real-time case information.Scientific data · 2020Article
- What can urban mobility data reveal about the spatial distribution of infection in a single city?BMC public health · 2019Article
- Forecasting national and regional influenza-like illness for the USA.PLoS computational biology · 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
- Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches.Nature communications · 2019Article
- Influenza forecast optimization when using different surveillance data types and geographic scale.Influenza and other respiratory viruses · 2018Article
- Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009-2014.Emerging infectious diseases · 2018Article
- Model selection for seasonal influenza forecasting.Infectious Disease Modelling · 2017Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, since 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, and we have previously tailored these methods for metropolitan Melbourne (Australia) and Google Flu Trends data. Here we extend these methods to clinical observation and laboratory-confirmation data for Melbourne, on the grounds that these data sources provide more accurate characterizations of influenza activity. We show that from each of these data sources we can accurately predict the timing of the epidemic peak 4-6 weeks in advance. We also show that making simultaneous use of multiple surveillance systems to improve forecast skill remains a fundamental challenge. Disparate systems provide complementary characterizations of disease activity, which may or may not be comparable, and it is unclear how a 'ground truth' for evaluating forecasts against these multiple characterizations might be defined. These findings are a significant step towards making optimal use of routine surveillance data for outbreak forecasting.
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