ArticlePloS one2025
Structural time series modelling for weekly forecasting of enterovirus outpatient, inpatient, and emergency department visits.
Article in PloS one, 2025. 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
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
2 citing papers in PubMed.
- CEEMDAN-decomposed time series forecasting of reported hepatitis B cases using KOA-optimised deep learning: a nationwide study in mainland China (2004-2027).Journal of global health · 2026Article
- Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China.Tropical medicine and health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesEnteroviruses pose a substantial public health challenge in Taiwan, often leading to increased healthcare visits. This study utilizes Taiwan CDC databases to analyse weekly enterovirus case data from emergency departments (EDs), as well as outpatient and inpatient settings. The objectives are to understand infection patterns through model fitting, forecast future visits for proactive epidemic management, and improve forecast accuracy by incorporating holiday effects. This approach enhances the reliability of predictions, supporting timely and effective surveillance and early detection of significant case surges.
methodsThis study divides the time series data into an in-sample period (2016-2023) and an out-of-sample period covering weeks 1 to 27 in 2024. Using an expanding window approach, the analysis applies Bayesian structural time series (BSTS) models, exponential smoothing, and random forest to forecast one-week-ahead cases over the 27 weeks in 2024. The study evaluates forecast accuracy using five key metrics and identifies significant surges in cases by detecting values that exceed the 95% prediction intervals, enhancing anomaly detection.
resultsThe results demonstrate that BSTS models, which incorporate trends, seasonal variations, summer, and Lunar New Year holiday effects, achieve superior forecasting accuracy. Specifically, by accounting for the Lunar New Year holiday within the out-of-sample period, the models attain mean absolute percentage error (MAPE) values of 6.509% for non-ED visits and 12.645% for ED visits.
conclusionsThe BSTS model effectively addresses nonlinearity and non-stationarity and adapts well to structural changes. This study highlights the importance of holiday adjustments, particularly for the Lunar New Year, in improving forecast accuracy during periods of unusual healthcare demand. These adjustments enhance the BSTS model performance for predicting irregular healthcare service demand.
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.