| dc.description.abstract | Pulmonary tuberculosis is one of the leading infectious diseases causing mortality worldwide, and its impact is exacerbated by opportunistic infections such as pneumonia. The lethal synergism between pulmonary tuberculosis and pneumonia contributes to increased mortality in endemic regions. Existing models of pulmonary tuberculosis do not incorporate the screening of asymptomatic infectious individuals, despite their significant contribution to disease transmission within the population. Additionally, these models do not account for the impact of natural immunity in controlling or reducing the spread of infections. The aim of this study was to model pulmonary tuberculosis and pneumonia co-infection, incorporating an asymptomatic infectious component and natural immunity. The study objectives included: formulating a pulmonary tuberculosis model that incorporates an asymptomatic infectious component and natural immunity; formulating a coinfection model that also incorporates an asymptomatic infectious component and natural immunity; determining the reproduction numbers for analyzing the formulated models; and investigating the effects of varying model parameters on the control of pulmonary tuberculosis. A population-based compartmental approach was employed to formulate the models, from which differential equations were derived. The progression from latent infection to pulmonary tuberculosis disease was modeled using Holling’s saturation term to account for natural immunity. The Next-Generation Matrix method was used to determine reproduction numbers, while stability was analyzed using the Routh–Hurwitz criteria, the Castillo–Chávez method, and Lyapunov functions. Sensitivity analyses of the reproduction numbers in relation to the model parameters were conducted using the normalized sensitivity index method. The model equations were solved numerically using fourth- and fifth-order Runge-Kutta methods in MATLAB software to identify effective intervention strategies for significantly reducing disease transmission. Secondary data from National Tuberculosis, Leprosy, and Lung Disease Program (NTLLDP) in Kenya were utilized to perform numerical simulations, with 2022 data serving as the initial conditions. The co-infection model was fitted to NTLLDP prevalence data from 2012 to 2022 using the fminsearch algorithm in MATLAB software to estimate specific parameters. The results predicted that combining vaccination, screening, and treatment for all forms of pulmonary tuberculosis is the most effective intervention for reducing transmission. Enhancing natural immunity, increasing screening rates for latently infected individuals, and improving vaccine efficacy were also shown to reduce the co-infected population. Specifically, a 10% increase in vaccine efficacy was predicted to reduce co-infections by 6.67%, while the same increase in the screening rate for latent infections reduced them by 6.73%. Furthermore, doubling the screening rate for individuals with asymptomatic infectious pulmonary tuberculosis was shown to reduce the population with severe co-infections and those undergoing treatment for severe conditions by 53.2%. These findings offer valuable guidance to healthcare officials in making informed decisions about screening latently infected and asymptomatic infectious tuberculosis patients, thereby contributing to the fight against epidemics of this disease. The results also underscore the importance of accurate diagnosis and effective treatment of tuberculosis and pneumonia co-infections, which contribute to reducing infection transmission. Lastly, the study emphasizes the need to enhance the natural immunity of latently infected individuals to reduce disease progression and transmission. | en_US |
