ArticleRisk management and healthcare policy2026
Forecasting Acute Hemorrhagic Conjunctivitis Incidence in Henan, China: A Comparative Study of Seasonal Autoregressive Fractionally Integrated Moving Average and Seasonal Autoregressive Integrated Moving Average Models.
Article in Risk management and healthcare policy, 2026. 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Acute hemorrhagic conjunctivitis (AHC) is a highly contagious viral disease causing significant public health burden. Accurate forecasting is essential for timely intervention. The seasonal autoregressive integrated moving average (SARIMA) model is widely used, but the seasonal autoregressive fractionally integrated moving average (SARFIMA) model, which captures long-range dependence, may perform better for complex incidence series. This study compared the forecasting performance of SARFIMA and SARIMA models for AHC in Henan. Methods: Monthly AHC case data from January 2011 to June 2023 in Henan Province were analyzed. Data from January 2011 to June 2022 were used for model training, with the remaining 12 months reserved for testing. National-level data from mainland China were used for external validation. Model performance was assessed using mean absolute deviation (MAD), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean error rate (MER). Results: AHC incidence showed a significant upward trend (average annual percentage change: 6.973%, 95% CI: 3.963%-10.071%) and a pronounced seasonal peak from March to September. The optimal models were SARIMA(0,1,2)(0,1,1) Conclusion: The SARFIMA model offers improved forecasting accuracy over SARIMA for AHC incidence in most metrics, suggesting its potential utility for public health surveillance. However, its application requires sufficient data length, and prospective validation is needed before operational deployment.
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.