ArticleBMC medical informatics and decision making2016
Predicting influenza with dynamical methods.
Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Synthetic method of analogues for emerging infectious disease forecasting.PLoS computational biology · 2025Article
- Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009-2014.Emerging infectious diseases · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrediction of influenza weeks in advance can be a useful tool in the management of cases and in the early recognition of pandemic influenza seasons.
methodsThis study explores the prediction of influenza-like-illness incidence using both epidemiological and climate data. It uses Lorenz's well-known Method of Analogues, but with two novel improvements. Firstly, it determines internal parameters using the implicit near-neighbor distances in the data, and secondly, it employs climate data (mean dew point) to screen analogue near-neighbors and capture the hidden dynamics of disease spread.
resultsThese improvements result in the ability to forecast, four weeks in advance, the total number of cases and the incidence at the peak with increased accuracy. In most locations the total number of cases per year and the incidence at the peak are forecast with less than 15 % root-mean-square (RMS) Error, and in some locations with less than 10 % RMS Error.
conclusionsThe use of additional variables that contribute to the dynamics of influenza spread can greatly improve prediction accuracy.
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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.