ArticlePloS one2021
Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Evaluating the contribution of weather variables to machine learning forecasts of visceral leishmaniasis in Brazil.Environmental research, health : ERH · 2025Article
- Investigation of tuberculosis incidence and particulate matter concentration in the middle east.Scientific reports · 2025Article
- Hand, Foot, and Mouth Disease Risk Prediction in Southern China: Time Series Study Integrating Web-Based Search and Epidemiological Surveillance Data.JMIR infodemiology · 2025Article
- Unraveling the drivers of leptospirosis risk in Thailand using machine learning.PLoS neglected tropical diseases · 2025Article
- Impact of meteorological factors on the incidence of hand, foot and mouth disease in Ningbo from 2014 to 2019: a causal convolutional neural networks.BMC public health · 2025Article
- Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.Scientific reports · 2025Article
- Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq.PloS one · 2025Article
- Effect of EV71 Vaccination on Transmission Dynamics of Hand, Foot, and Mouth Disease and Its Epidemic Prevention Threshold.Vaccines · 2024Article
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- Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine.BMC chemistry · 2024Article
- Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.Frontiers in cardiovascular medicine · 2024Article
- Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China.PLoS neglected tropical diseases · 2023Article
- Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.BMC public health · 2023Article
- Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.International journal of environmental research and public health · 2023Article
- Nonlinear Effects of the Neighborhood Environments on Residents' Mental Health.International journal of environmental research and public health · 2022Article
- A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers.International journal of environmental research and public health · 2022Article
- Prediction of adolescent suicidal ideation after the COVID-19 pandemic: A nationwide survey of a representative sample of Korea.Frontiers in pediatrics · 2022Article
- School outbreak of coxsackievirus A16 in Antipolo City, Philippines, October 2022.Western Pacific surveillance and response journal : WPSARArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analysis and prediction. We first used Random Forest to examine the association between HFMD incidence and potential influential factors for 31 provinces in mainland China. Next, we established Random Forest and XGBoost prediction models using meteorological and social factors as the predictors. Finally, we applied our prediction models in four different regions of mainland China and evaluated the performance of them. Our results show that: 1) Meteorological factors and social factors jointly affect the incidence of HFMD in mainland China. Average temperature and population density are the two most significant influential factors; 2) Population flux has different delayed effect in affecting HFMD incidence in different regions. From a national perspective, the model using population flux data delayed for one month has better prediction performance; 3) The prediction capability of XGBoost model was better than that of Random Forest model from the overall perspective. XGBoost model is more suitable for predicting the incidence of HFMD in mainland China.
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