ArticleInfectious Disease Modelling2017
Model selection for seasonal influenza forecasting.
Article in Infectious Disease Modelling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 19 citations in OpenAlex.
- Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season.PloS one · 2023Article
- Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves.PloS one · 2021Article
- Article
- 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 national and regional influenza-like illness for the USA.PLoS computational biology · 2019Article
- Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.Tropical medicine and infectious disease · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemics of seasonal influenza inflict a huge burden in temperate climes such as Melbourne (Australia) where there is also significant variability in their timing and magnitude. Particle filters combined with mechanistic transmission models for the spread of influenza have emerged as a popular method for forecasting the progression of these epidemics. Despite extensive research it is still unclear what the optimal models are for forecasting influenza, and how one even measures forecast performance. In this paper, we present a likelihood-based method, akin to Bayes factors, for model selection when the aim is to select for predictive skill. Here, "predictive skill" is measured by the probability of the data
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.