Integrating Optimal Control and Economic Analysis to Inform Marburg Virus Response: A Comparison of Intervention Packages

Raed Qahiti, Gizachew Kefelew Hailu, Ali H. Hakami, Ahmad Albaity and Shewafera Wondimagegnhu Teklu

Journal of Mathematics, 2026, vol. 2026, 1-33

Abstract: Marburg virus disease is a severe hemorrhagic illness caused by filoviruses that kills roughly half of those it infects, yet no licensed vaccine or specific antiviral treatment currently exists. When an outbreak strikes a resource-poor region, health officials face an agonizing question: which interventions offer the most protection for the least cost? This study confronts that dilemma by developing a deterministic compartmental model that tracks disease transmission through symptomatic, hospitalized, and deceased individuals, the last crucial pathway often neglected in filovirus models. We combine optimal control theory with rigorous cost-effectiveness analysis to evaluate three interventions: screening and isolation, contact reduction with infectious fluids, and enhanced clinical management. Solving the resulting two-point boundary value problem using forward–backward sweep methods with fourth-order Runge–Kutta integration, we compare dual and triple control strategies against a baseline of no intervention. Our sensitivity analyses, both local and global, identify community and hospital transmission rates as the dominant outbreak drivers, while burial practices emerge as the strongest leverage point for control. The findings reveal a counterintuitive truth: the epidemiologically most powerful strategy, deploying all three interventions simultaneously, is not the most economical. Instead, pairing screening with contact reduction prevents over 5500 infections at an incremental cost of just 12 cents per case averted, offering far better value than chasing marginal gains with costlier packages. We conclude that maximum disease suppression and optimal resource allocation seldom align and that mathematical modeling must integrate economic reality to guide evidence-based response.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/jmath/2026/9697795.pdf (application/pdf)
http://downloads.hindawi.com/journals/jmath/2026/9697795.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:9697795

DOI: 10.1155/jom/9697795

Access Statistics for this article

More articles in Journal of Mathematics from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().