ArticleInfectious Disease Modelling2025
Predictive and early warning analysis of infectious gastroenteritis based on the BiLSTM-BiGRU model.
Article in Infectious Disease Modelling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Appropriate use of scientific early-warning infectious disease surveillance methods plays a vital role in disease control and prevention. Recently infectious gastroenteritis has become an important public health problem. In consideration of meteorological factors strongly linked with the incidence of infectious gastroenteritis, we obtained data on the number of infectious gastroenteritis cases and meteorological data from January 2008 to June 2023, a total of 808 weeks. We constructed a BiLSTM-BiGRU model to fit and predict the incidence of infectious gastroenteritis in Tokyo, Japan, to improve the prediction accuracy and early warning efficiency of infectious gastroenteritis, provide references for relevant departments to formulate infectious disease prevention and control measures in advance, and make emergency preparations. For this purpose, we also used three optimization algorithms for parameter tuning and constructed a moving percentile control chart warning model. The results show that the BiLSTM-BiGRU model performed better than mainstream deep learning methods. Among the three selected optimization algorithms, the Grey Wolf Optimization algorithm performed the best, with an R
Indexed as
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.