Impact of Public Health Interventions on the Co-infection Dynamics of COVID-19 with Tuberculosis

Abstract

COVID-19 and tuberculosis (TB) co-infection presents a significant public health challenge by increasing disease burden and complicating disease management, diagnosis, and treatment. To explore the COVID-19-TB co-infection dynamics and evaluate interventions such as public health campaigns, mask wearing, and social distancing, we formulate and analyse a novel ten-compartmental deterministic SEIRS model. The model is unique in the sense that it simultaneously includes separate exposed, infectious, and recovered classes for TB, COVID-19 and co-infected individuals, thus providing a detailed description of the disease progression, immunity loss, and co-infection routes. The analysis proceeds by first examining the reduced sub-models corresponding to COVID-19-only and TB-only dynamics, followed by the complete co-infection model. For each case, both local and global stability properties are rigorously investigated using analytical techniques complemented by numerical verification. Model calibration is performed against cumulative weekly COVID-19 case data from Brazil and South Africa to estimate the best-fit model parameters. Numerical simulations reveal that reduction in contact rates substantially decreases the prevalence of both single infections and co-infections. Furthermore, public health campaigns are particularly effective in reducing TB transmission and levels of co-infection, whereas mask wearing and social distancing suppress COVID-19 spread and its associated co-infections. Our results suggest that implementing disease-specific interventions can help reduce the disease burden and offer evidence-based information for the development of effective public health policies in areas where both diseases are endemic.