A comparative mathematical modeling study of social and environmental influences on tuberculosis transmission in Brazil, the USA, and Canada

Tuberculosis (TB) continues to pose major global health challenges, with recent resurgences in the United States, localized outbreaks in Canada, and a persistently high incidence in Brazil. We present a novel deterministic compartmental model that integrates both behavioral awareness and environmental transmission into the dynamics of TB. The model subdivides the population into susceptible, exposed, infectious, and recovered classes, with an additional compartment capturing the persistence of Mycobacterium tuberculosis in the environment. Behavioral awareness is explicitly modeled as a dynamic factor influencing transmission through social response and media influence. Parameter estimation was performed using Particle Swarm Optimization (PSO) on country-level incidence data. Model validation demonstrated the superior predictive performance of generalized additive models (GAMs) compared with generalized linear models (GLMs), especially in Canada and Brazil, where the TB trajectories are nonlinear. The sensitivity analysis identified latent progression ( $$\sigma$$ ), recovery rate ( $$\gamma$$ ), and the maximum effect of awareness ( $$\kappa$$ ), as the most influential parameters. Heatmap simulations revealed that enhanced awareness and behavioral change can drive the control reproduction number below unity, even under high transmission conditions. Our findings highlight the importance of integrating biomedical interventions with behavioral adaptation and environmental sanitation in TB control strategies in various epidemiological countries.