Modeling the effects of treatment adherence challenges on the transmission dynamics of hepatitis C virus

Abstract

Infectious disease modeling is crucial for predicting disease progression over time and helps guide decision makers in public health policy. Hepatitis C virus (HCV) prevalence is still increasing in Zimbabwe, a low-middle-income country (LMIC), despite the availability of effective treatments, and the reasons for this increase are not well understood. Our study employed a mathematical model to explain the impact of poor treatment adherence on HCV transmission dynamics in Zimbabwe. We computed the basic reproduction number (ℛ0), a vital metric of disease spread. Equilibrium states of the model were determined, and their stability was investigated. The study demonstrated that an adherence level exceeding 52% causes the reproduction number to drop below 1, curtailing further spread. Our HCV model indicates that variations in re-susceptibility minimally impact outcomes, suggesting that re-susceptibility can often be excluded in such analyses. Our model unraveled the synergistic impact of simultaneously enhancing the recovery rate of acutely infected individuals and treatment adherence on reducing ℛ0. The study underlines the pressing need for stronger health interventions, including patient education, financial assistance, and rigorous monitoring, to improve treatment adherence. These interventions are paramount in curbing HCV proliferation, particularly in LMICs like Zimbabwe, and can serve as a template for similar settings globally.