Fig 1.
Backward bifurcation diagram for of model 3, using the parameter values shown in Table 1.
EE denotes endemic equilibrium and DFE denotes disease-free equilibrium.
Table 1.
Model parameters and their interpretations.
Fig 2.
Data fitting results for the HCV dynamics model.
The blue markers represent observed data points, while the red line illustrates the model’s predictions. The close alignment between the two shows the model’s accuracy in capturing the underlying dynamics of HCV transmission and progression. The model was fitted to the data from Zimbabwe over a period of 30 years between the years 1990 - 2019, which can be obtained from: https://www.globalhep.org/country-progress/zimbabwe [29], using initial conditions as follows . The root mean square (RMS) was calculated as 0.0001.
Fig 3.
Data fitting results for the HCV dynamics model.
The blue markers represent observed data points, while the red line illustrates the model’s predictions. This is an extension of the model fits Fig 3, with projections for the year 2019 to 2028.
Fig 4.
Presents a histogram of the basic reproduction number , computed from 10000 samples using LHS.
The distribution highlights the range and frequency of different values, illustrating their sensitivity to variations in the model parameters listed in Table 1. Notably, the mean value of
across these samples was estimated to be 2.19. The sampling ranges used are as indicated in Table 1.
Fig 5.
Partial Rank Correlation Coefficient (PRCC) sensitivity analysis results, obtained using LHS with 1000 samples and run in Python version 3.11.0.
This highlights the relative influence of model parameters from Table 1 on the basic reproduction number . Parameters with positive PRCCs will increase
when they increase, while those with negative PRCCs will decrease
as they increase. The sampling ranges used are as indicated in Table 1.
Fig 6.
Impact of varying the effective contact rates , on the reproduction number
.
Fig 7.
Illustrates the effects of varying: (a) the treatment adherence proportion , and (b) the recovery rate for individuals with chronic infections ρ on the reproduction number
.
In this analysis, ρ and were varied while all other parameters were held constant as displayed in Table 1.
Fig 8.
Impact of recovery rate of the chronically infected ρ and the proportion adherence on the reproduction number
.
This contour plot shows how ρ and affect the reproduction number in HCV treatment.
Fig 9.
Effect of varying the proportion of individuals adhering to treatment on the acutely infected cases I, with (a)
and (b)
.
Fig 10.
Effect of varying the proportion of individuals adhering to treatment, represented by , on the number of chronically infected individuals who are under treatment, denoted by C, with (a)
and (b)
.
Fig 11.
Effect of varying the proportion of individuals adhering to treatment, , on the number of chronically infected individuals who are not under treatment, denoted by Cq, with (a)
and (b)
.
Fig 12.
Effect of varying the re-susceptibility rates (ϕ) on infection dynamics across varying scenarios of the basic reproduction number ().
Subplots (a) and (b) show the number of acutely infected individuals without treatment (I) for and
respectively. Subplots (c) and (d) depicts the chronically infected individuals under treatment (I) for the corresponding
scenarios. Lastly, subplots (e) and (f) illustrate the dynamics of chronically infected individuals not under treatment (Cq) under
and
conditions. This figure offers insights into the intricate relationship between treatment adherence and infection outcomes in diverse epidemiological settings.