A compartmental deterministic epidemiological model with non-linear differential equations for analyzing the co-infection dynamics between COVID-19, HIV, and Monkeypox diseases

Abstract

One of the realities of the COVID-19 worldwide pandemic is the occurrence of infected individuals with COVID-19 and two other diseases, Monkeypox and HIV. This study presents a compartmental deterministic epidemiological model with non-linear differential equations to study the transmission dynamics of the co-infection of the three diseases. Rigorous analysis of the model shows that the disease-free equilibrium was locally and globally asymptotically stable when the associated reproduction number of the diseases was not up to unity, showing that the spread of the diseases and their co-circulation can be effectively controlled in this circumstance. Real-life data about the diseases are collated and fitted to the model through which values of key parameters of the model were estimated. These parameters’ values were used to carry out numerical simulations of the model using MATLAB and validate the qualitative results obtained earlier from the model. The numerical simulation of the model was used to explore the interactions and dynamics resulting from the co-infection of COVID-19, HIV, and Monkeypox in humans, including the reciprocal impacts of each of the diseases on the other two, their patterns of coexistence and their effects on the host. We developed a tool to help predict the co-infection of the three diseases. Through the insights gained in this study, recommendations were made to policymakers in the healthcare sector on how to combat effectively and adequately the co-infection of the three diseases in the human population and mitigate their disease burden.