ArticleInfection and drug resistance2019
Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses.
Article in Infection and drug resistance, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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51 citing papers in PubMed.
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- A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data.PloS one · 2025Article
- Predicting the incidence of rifampicin resistant tuberculosis in Yunnan, China: a seasonal time series analysis based on routine surveillance data.BMC infectious diseases · 2024Article
- Estimating the Temporal Epidemiological Trends of Tuberculosis Incidence by Using an Advanced Theta Method.The American journal of tropical medicine and hygiene · 2024Article
- Spatial-temporal distribution characteristics of pulmonary tuberculosis in eastern China from 2011 to 2021.Epidemiology and infection · 2024Article
- Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.BMC public health · 2024Article
- Integer time series models for tuberculosis in Africa.Scientific reports · 2023Article
- Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.International journal of environmental research and public health · 2023Article
- Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.Neural computing & applications · 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
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- Mathematical model to predict COVID-19 mortality rate.Infectious Disease Modelling · 2022Article
- Mathematical Models Supporting Control of COVID-19.China CDC weekly · 2022Article
- Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China.BMC infectious diseases · 2022Article
- Development and comparison of predictive models for sexually transmitted diseases-AIDS, gonorrhea, and syphilis in China, 2011-2021.Frontiers in public health · 2022Article
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8 authors.
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Abstract
objectiveForecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China.
methodsWe applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect.
resultsDuring 2005-2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1)
conclusionBoth the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.
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