ArticlePloS one2022
The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Optimizing Tuberculosis Incidence Prediction: A Systematic Review of Hybrid Modeling Approaches and Machine Learning Techniques.Public health challenges · 2026Review
- A novel time-series modeling framework for predicting tuberculosis incidence in Sichuan, China.BMC infectious diseases · 2026Article
- Predictive modeling of medical waste and a proposal to improve segregation in a peruvian hospital.Scientific reports · 2026Article
- Forecasting monthly AIDS incidence in China via LSTM-CNN parallel fusion: a comparative study of 10 predictive models.Frontiers in public health · 2026Article
- Reflections on predictive modeling for infectious diseases.Frontiers in public health · 2026Article
- Epidemiological characteristics and incidence prediction of varicella from 2014 to 2023 in Chongqing, China.Frontiers in public health · 2026Article
- Predicting the prevalence and estimating the economic burden of ischemic heart disease in China: Based on long short-term memory model.Medicine · 2025Article
- Forecasting antimicrobial resistance in China using a hybrid ARIMA-GM(1,1) model.BMC infectious diseases · 2025Article
- Analysis of the trends and predictions of tuberculosis burden in China from 1990 to 2021 based on the GBD database.Frontiers in public health · 2025Article
- A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data.PloS one · 2025Article
- Spatial and temporal analysis and forecasting of TB reported incidence in western China.BMC public health · 2024Article
- A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection.Scientific reports · 2024Article
- Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China.Iranian journal of public health · 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
- A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study.Frontiers in cellular and infection microbiology · 2023Article
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7 authors.
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Abstract
BACKGROUND AND
objectiveTuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China.
methodsThe data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People's Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy.
resultsThere were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models.
conclusionsOur findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.
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