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ORIGINAL RESEARCH article

Front. Public Health

Sec. Infectious Diseases: Epidemiology and Prevention

Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1614339

This article is part of the Research TopicWorld TB Day 2025: Yes! We Can End TB: Commit, Invest, DeliverView all articles

Feasibility of Ending Tuberculosis in Shangrao City through Active Intervention Measures: A Mathematical Study

Provisionally accepted
Mingshu  XuMingshu Xu1Yue  HeYue He2Qiao  LiuQiao Liu2Qiuping  ChenQiuping Chen2,3,4Zeyu  ZhaoZeyu Zhao2Zheng  XuZheng Xu1Chongfei  ShuChongfei Shu1Jun  XiaJun Xia1Yuyan  YangYuyan Yang1Laurent  GavotteLaurent Gavotte5Roger  FrutosRoger Frutos3Huiming  YeHuiming Ye6Yanhua  SuYanhua Su2Xiaolan  WangXiaolan Wang7*Zhen  LiuZhen Liu1*
  • 1Shangrao Centre for Disease Control and Prevention, Shangrao, China
  • 2Xiamen University School of Public Health, Xiamen, China
  • 3CIRAD, URM 17, Intertryp, Montpellier, France
  • 4Université de Montpellier, Montpellier, France
  • 5Espace-Dev, Université de Montpellier, Montpellier, France
  • 6Department of Laboratory Medicine, Fujian Key Clinical Specialty of Laboratory Medicine, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen, China
  • 7Shangrao People's Hospital, Shangrao, Jiangxi Province, China

The final, formatted version of the article will be published soon.

Objective: China faces significant challenges in ending tuberculosis (TB). Active case finding (ACF) and TB preventive therapy (TPT) have proven to be critical measures in reducing TB incidence. This study uses a transmission dynamics model to identify the optimal intervention strategies for achieving WHO ' s TB elimination targets in Shangrao City. The findings guide targeted TB control efforts in similar settings.: To account for COVID-19 pandemic disruptions, we first used a seasonal autoregressive integrated moving average (SARIMA) model to predict and substitute the reported TB incidence during 2020-2023. Subsequently, we developed an age-stratified dynamic transmission model using surveillance data from Shangrao City's Infectious Diseases Reporting System (IDRS) between 2008 and 2023 to evaluate tuberculosis transmission patterns across age groups. The model assessed the effectiveness of key interventions including active case finding (ACF), latent tuberculosis infection (LTBI) screening, and tuberculosis preventive treatment (TPT).The model fit well with the reported data (R 2 = 0.53, p < 0.001). Preventive treatment measures can fully achieve the goal of reducing incidence. All five TPT regimens showed potential to meet the TB elimination targets, with the 3HP regimen (weekly rifapentine + isoniazid for 3 months) performing the best. With the proportion of post-detection consent to TPT of 0.6 and rate of LTBI screening of 0.5, the 3HP regimen met the 2030 and 2035 incidence targets, with projected rates of 15.27/100,000 and 7.98/100,000, respectively.The current TB control efforts face significant challenges, with a considerable gap remaining in achieving TB elimination targets. Combining ACF with TPT presents a promising strategy to reach these goals. Elderly tuberculosis (TB) patients constitute a high-risk population, and effective prevention and treatment in this group are critical to achieving future TB elimination goals. To reduce the risk of recurrence and reinfection, enhanced follow-up monitoring of elderly patients should be prioritised alongside targeted health education interventions tailored to high-risk groups.

Keywords: Tuberculosis, dynamic model, End tuberculosis, Active case finding, Tuberculosis preventive therapy

Received: 18 Apr 2025; Accepted: 21 Aug 2025.

Copyright: © 2025 Xu, He, Liu, Chen, Zhao, Xu, Shu, Xia, Yang, Gavotte, Frutos, Ye, Su, Wang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Xiaolan Wang, Shangrao People's Hospital, Shangrao, 334000, Jiangxi Province, China
Zhen Liu, Shangrao Centre for Disease Control and Prevention, Shangrao, China

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