Impact of population migration and seasonality on tuberculosis transmission: A multi-regional dynamic modeling and cost-effectiveness analysis in Jiangsu Province, China

{\bf Background}Tuberculosis (TB) remains a significant public health challenge in Jiangsu, China, influenced by population migration and seasonality. This study integrates epidemiological modeling with cost-effectiveness analysis to evaluate the impact of these factors on TB transmission and proposes optimized intervention strategies. \\{\bf Objective}Based on the epidemic situation and epidemiological characteristics of tuberculosis in Jiangsu Province, this paper established a multi-regional ‌\((SCLIT)\) model incorporating seasonality, population migration (both within and outside Jiangsu), fast/slow disease progression, reinfection, imperfect treatment, and vaccination. This model aims to analyze the risk factors for tuberculosis epidemics and identify the most cost-effective control strategies.\\{\bf Methods}Using tuberculosis case data and migration records from 2009 to 2019, we fitted the dynamic model parameters and analyzed their sensitivity to the average basic reproduction number (\(({\bar {\mathcal{R}_0}})\)) and infected populations. Ignoring the impact of COVID-19, we also used the model to predict the number of tuberculosis outbreaks in Jiangsu Province as a whole from 2023 to 2025, and the predicted trends were largely consistent with the actual data. Combined with the actual epidemiological situation of tuberculosis in Jiangsu, we simulated population mobility and conducted a detailed study on tuberculosis prevention and control in the province through cost-effectiveness analysis.\\{\bf Results}The results showed that (\(({\bar {\mathcal{R}_0}})\)) for TB in Jiangsu was ‌3.5633‌ (95% CI: 3.4988–3.6278), with an epidemic cycle of approximately one year. Compared to baseline methods, the most cost-effective strategy could prevent ‌16,380 total infections‌ (1.7%), reduce ‌62,880 deaths‌, and avoid ‌3,269,500 DALYs, while cutting total costs by ‌461.65 million‌ (14.4% reduction). Incorporating climatic factors and population mobility dynamics enables the tuberculosis model to capture the long-term outbreak patterns in greater detail. During peak travel periods (e.g., holiday migration and Spring Festival travel), high-density population movement increases the risk of outbreaks. In contrast, mobility restrictions have no significant impact on tuberculosis control.