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Tuberculosis and COVID-19 are among the diseases with major global public health concern and great socio-economic impact. Co-infection of these two diseases is inevitable due to their geographical overlap, a potential double blow as their clinical similarities could hamper strategies to mitigate their spread and transmission dynamics. To theoretically investigate the impact of control measures on their long-term dynamics, we formulate and analyze a mathematical model for the co-infection of COVID-19 and tuberculosis. Basic properties of the tuberculosis only and COVID-19 only sub-models are investigated as well as bifurcation analysis (possibility of the co-existence of the disease-free and endemic equilibria). The disease-free and endemic equilibria are globally asymptotically stable. The model is extended into an optimal control system by incorporating five control measures. These are: tuberculosis awareness campaign, prevention against COVID-19 (e.g., face mask, physical distancing), control against co-infection, tuberculosis and COVID-19 treatment. Five strategies which are combinations of the control measures are investigated. Strategy B which focuses on COVID-19 prevention, treatment and control of co-infection yields a better outcome in terms of the number of COVID-19 cases prevented at a lower percentage of the total cost of this strategy.

A new mathematical model for COVID-19 and HIV/AIDS is considered to assess the impact of COVID-19 on HIV dynamics and vice-versa. Investigating the epidemiologic synergy between COVID-19 and HIV is important. The dynamics of the full model is driven by that of its sub-models; therefore, basic analysis of the two sub-models; HIV-only and COVID-19 only is carried out. The basic reproduction number is computed and used to prove local and global asymptotic stability of the sub-models’ disease-free and endemic equilibria. Using the fmincon function in the Optimization Toolbox of MATLAB, the model is fitted to real COVID-19 data set from South Africa. The impact of intervention measures, namely, COVID-19 and HIV prevention interventions and COVID-19 treatment are incorporated into the model using time-dependent controls. It is observed that HIV prevention measures can significantly reduce the burden of co-infections with COVID-19, while effective treatment of COVID-19 could reduce co-infections with opportunistic infections such as HIV/AIDS. In particular, the COVID-19 only prevention strategy averted about 10,500 new co-infection cases, with similar number also averted by the HIV-only prevention control.