To model the dynamics of Hepatitis C virus (HCV) transmission using fractional order approaches and physics-informed neural networks, highlighting the significance of these methods in improving understanding and intervention strategies.
Key Findings:
HCV infection remains a significant public health issue, particularly in low-income countries like Zimbabwe, with implications for treatment access and health outcomes.
Current treatment access is severely limited despite the availability of effective directly acting antivirals (DAAs), highlighting the need for improved healthcare policies.
Fractional-order models provide a more realistic representation of HCV dynamics compared to classical models, suggesting a shift in modeling approaches could enhance understanding.
Interpretation:
The use of fractional-order dynamics allows for a better understanding of HCV transmission and treatment effects, potentially improving intervention strategies and informing public health policies.
Limitations:
Lack of comprehensive national estimates for HCV prevalence and mortality in Zimbabwe, which hampers effective policy-making.
Challenges in data collection and modeling due to underreporting and stigma associated with HCV, necessitating innovative data-gathering approaches.
Conclusion:
Fractional-order modeling could enhance the effectiveness of HCV elimination strategies by accurately reflecting the complexities of disease dynamics, underscoring the urgency for its implementation.
