ArticleEpidemiology and infection2023
Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China.
Article in Epidemiology and infection, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Epidemiological characteristics and incidence prediction analysis of brucellosis in Bayingolin mongol autonomous prefecture, Xinjiang.BMC infectious diseases · 2026Article
- Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data.JMIR infodemiology · 2025Article
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Time series analysis and seasonality trends of SARS-CoV-2 in Ecuador (2020-2024): a four-year study.Frontiers in public health · 2025Article
- Incidence of acute hemorrhagic conjunctivitis in Chongqing: a forecasting study based on mathematical models.Frontiers in public health · 2025Article
- Hybrid models combining trend and seasonality components with machine learning algorithms provide accurate forecasting of malaria incidence.PLOS global public health · 2025Article
- Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq.PloS one · 2025Article
- Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study.BMC public health · 2024Article
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4 authors.
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Abstract
Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease. The incidence of HFMD has a pronounced seasonal tendency and is closely related to meteorological factors such as temperature, rainfall, and wind speed. In this paper, we propose a combined SARIMA-XGBoost model to improve the prediction accuracy of HFMD in 15 regions of Xinjiang, China. The SARIMA model is used for seasonal trends, and the XGBoost algorithm is applied for the nonlinear effects of meteorological factors. The geographical and temporal weighted regression model is designed to analyze the influence of meteorological factors from temporal and spatial perspectives. The analysis results show that the HFMD exhibits seasonal characteristics, peaking from May to August each year, and the HFMD incidence has significant spatial heterogeneity. The meteorological factors affecting the spread of HFMD vary among regions. Temperature and daylight significantly impact the transmission of the disease in most areas. Based on the verification experiment of forecasting, the proposed SARIMA-XGBoost model is superior to other models in accuracy, especially in regions with a high incidence of HFMD.
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