Dynamic Analysis of Drug-Resistant Tuberculosis Transmission via Two Routes Based on Economic Effects

Tuberculosis, one of the oldest infectious diseases in the world, is caused by M. tuberculosis, and currently the infectious disease with the highest single-disease mortality rate. The invention of anti-tuberculosis drugs has aided in controlling tuberculosis, but drug resistance remains a significant challenge in its prevention and control. This study is based on tuberculosis reporting data released by the China CDC from 2004 to 2020. Considering that drug-resistant tuberculosis has both primary and acquired transmission routes, a dynamic model coupled with economic factors is established. Nonlinear least squares fitting is applied to existing reports of new cases to derive model parameters. Sensitivity analysis reveals that transmission coefficients, disease progression rates, and economic parameters significantly influence tuberculosis transmission. Different economic effect parameters exert differentiated intervention effects on the transmission of susceptible and drug-resistant strains, clinical outcomes, and strain conversion. Calculations indicate that under current control measures, the control reproduction numbers for tuberculosis transmission among drug-sensitive and drug-resistant populations are [Formula: see text] =0.5460, [Formula: see text] =0.6419, respectively. Given the current control measures and economic investment, it will be impossible to achieve the WHO’s goal of eliminating tuberculosis in China by 2035. Increased economic investment in high-prevalence areas, coupled with a focus on measures to interrupt human-to-human transmission, can effectively control tuberculosis outbreaks. In low-prevalence areas, increased investment prioritizing the reduction of disease progression yields superior outcomes. Furthermore, enhancing economic investment in adherence monitoring for drug-sensitive infections and improving conversion rates can reduce the number of drug-resistant cases while preventing a significant increase in drug-sensitive infections. This provides a basis for optimizing resource allocation and strategic planning of control measures.