ArticleBMC infectious diseases2020
Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province, China.
Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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31 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
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- Time-Series Analysis and Age-Stratified Forecasting of Diarrheal Disease in Rwanda Using SARIMA Models.Tropical medicine and infectious disease · 2026Article
- Real-time dynamic prediction of HFMD transmission using SEIRQ-ARIMA hybrid model optimized by multi-stage ABC-GWO algorithm.Scientific reports · 2026Article
- Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.Scientific reports · 2025Article
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Missing value imputation for > 2 MeV electron fluxes in geostationary orbit based on GA-RF model.Scientific reports · 2025Article
- Unveiling the future: Wavelet- ARIMAX analysis of climate and diarrhea dynamics in Bangladesh's Urban centers.BMC public health · 2025Article
- A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China.PloS one · 2025Article
- Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh.PLoS neglected tropical diseases · 2025Article
- Prediction of the burden of road traffic injuries in Iran by 2030: Prevalence, death, and disability-adjusted life years.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2024Article
- Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.BMC infectious diseases · 2024Article
- Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review.Learning health systems · 2024Article
- Etiological surveillance of viral diarrhea from 2017 to 2019 in Zhangzhou city, Fujian province, China.Frontiers in public health · 2024Article
- Epidemic Characteristics, Spatiotemporal Pattern, and Risk Factors of Other Infectious Diarrhea in Fujian Province From 2005 to 2021: Retrospective Analysis.JMIR public health and surveillance · 2023Article
- Analytical reference framework to analyze non-COVID-19 events.Population health metrics · 2023Article
- Telehealth utilization in U.S. medicare beneficiaries aged 65 years and older during the COVID-19 pandemic.BMC public health · 2023Article
- Predicting COVID-19 using lioness optimization algorithm and graph convolution network.Soft computing · 2023Article
- Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models.Journal of applied statistics · 2023Article
- The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study.BMC infectious diseases · 2022Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundInfectious diarrhea can lead to a considerable global disease burden. Thus, the accurate prediction of an infectious diarrhea epidemic is crucial for public health authorities. This study was aimed at developing an optimal random forest (RF) model, considering meteorological factors used to predict an incidence of infectious diarrhea in Jiangsu Province, China.
methodsAn RF model was developed and compared with classical autoregressive integrated moving average (ARIMA)/X models. Morbidity and meteorological data from 2012 to 2016 were used to construct the models and the data from 2017 were used for testing.
resultsThe RF model considered atmospheric pressure, precipitation, relative humidity, and their lagged terms, as well as 1-4 week lag morbidity and time variable as the predictors. Meanwhile, a univariate model ARIMA (1,0,1)(1,0,0)
conclusionsThe RF model fitted the dynamic nature of an infectious diarrhea epidemic well and delivered an ideal prediction accuracy. It comprehensively combined the synchronous and lagged effects of meteorological factors; it also integrated the autocorrelation and seasonality of the morbidity. The RF model can be used to predict the epidemic level and has a high potential for practical implementation.
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