Prediction of the pulmonary tuberculosis incidence and control measures assessment in China based on Bayesian method and seasonal dynamic model

LI Peiji; WANG Yayi; DAI Mengmeng; LIU Yingbo

doi:10.16462/j.cnki.zhjbkz.2024.04.001

Volume 28 Issue 4

Apr. 2024

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LI Peiji, WANG Yayi, DAI Mengmeng, LIU Yingbo. Prediction of the pulmonary tuberculosis incidence and control measures assessment in China based on Bayesian method and seasonal dynamic model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(4): 373-380. doi: 10.16462/j.cnki.zhjbkz.2024.04.001

Citation:

LI Peiji, WANG Yayi, DAI Mengmeng, LIU Yingbo. Prediction of the pulmonary tuberculosis incidence and control measures assessment in China based on Bayesian method and seasonal dynamic model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(4): 373-380. doi: 10.16462/j.cnki.zhjbkz.2024.04.001

Citation:

LI Peiji, WANG Yayi, DAI Mengmeng, LIU Yingbo. Prediction of the pulmonary tuberculosis incidence and control measures assessment in China based on Bayesian method and seasonal dynamic model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(4): 373-380. doi: 10.16462/j.cnki.zhjbkz.2024.04.001

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Prediction of the pulmonary tuberculosis incidence and control measures assessment in China based on Bayesian method and seasonal dynamic model

doi: 10.16462/j.cnki.zhjbkz.2024.04.001

Department of Biostatistics, School of Science, China Pharmaceutical University, Nanjing 211100, China

Funds:

Natural Science Foundation of Jiangsu Province BK20190549

More Information

Corresponding author: LIU Yingbo, E-mail: lyb11206@163.com
Received Date: 2023-07-24
Rev Recd Date: 2024-01-10

Available Online: 2024-05-17

Publish Date: 2024-04-10

Abstract

Abstract

Objective Considering the seasonal periodicity of pulmonary tuberculosis (PTB), a dynamic model is constructed to fit and predict the monthly PTB cases in China, providing a reference for relevant departments to optimize PTB prevention and control measures. Methods First, based on seasonal-trend decomposition using loess (STL) model and susceptible-vaccinated-early latent-late latent-infected-treated (SVELIT) model, a dynamic model (STL-SVELIT) was established. Then, the monthly PTB cases in China from 2017 to 2021 was used to fit the model. The parameters of the model and further the basic reproduction number (R₀) were estimated in the Bayesian framework, in order to predict the epidemic trend of PTB in China. In terms of intervention evaluation, sensitivity analysis based on R₀was conducted to simulate the prevention measures for PTB. Specifically, the effects of various PTB prevention and control measures were evaluated by reducing the value of parameters: β, which represents the disease transmission coefficient; θ₁, θ₂, which accounts for the progression rate from latent individuals to active PTB patients in both early and late stages; and p₃, which indicates the proportion of symptomatic cases. Results The Mean Absolute Percentage Error (MAPE) was 4.30% when using the monthly PTB cases from 2017 to 2021 to fit the STL-SVELIT model. MAPE was 6.57% when predicting the monthly PTB cases from January 2022 to May 2023, which has a good fitting effect and prediction accuracy. The model estimated that the R₀ of PTB in China was 2.076, suggesting that PTB remains prevalent in the population. Simulation results showed that the predicted PTB incidence in China will reach 29.1 per 100 000 in 2035 if β declined by 75.00%; 25.4 per 100 000 in 2035 if θ₁ and θ₂ both decreased by 75.00%; 11.1 per 100 000 in 2035 if parameters β, θ₁, θ₂ and p₃all declined by 75.00%. Conclusions Reducing the disease transmission coefficient and the rate of progression of the latent period is effective in controlling the PTB epidemic. However, comprehensive measures are required to achieve the WHO End TB Strategies target in 2035 (tuberculosis incidence below 10.0 per 100 000).
- Pulmonary tuberculosis,
- Seasonal decomposition,
- Transmission dynamics model,
- Bayesian theory,
- Prediction and evaluation

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References(15)

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