Process integration for emerging challenges: optimal allocation of antivirals under resource constraints

Area of Science:

• Operations Research
• Public Health
• Pharmacoeconomics

Background:

• The COVID-19 pandemic necessitates rapid development and deployment of pharmaceutical interventions, including repurposed drugs.
• Anticipated drug shortages pose significant challenges for equitable allocation and patient outcomes.
• Mathematical modeling offers a robust approach to address resource constraints in healthcare.

Purpose of the Study:

• To develop and present a linear programming model for optimizing the allocation of COVID-19 drugs.
• To minimize patient fatalities by considering critical hospital capacity constraints.
• To provide a decision-support tool for policymakers and medical personnel facing drug scarcity.

Main Methods:

• Development of a linear programming model based on process integration techniques.
• Incorporation of hospital capacity constraints into the allocation model.
• Solution of two hypothetical case studies to demonstrate model efficacy.

Main Results:

• The model successfully determined optimal drug allocation strategies.
• Demonstrated computational capability in generating allocation plans.
• Achieved superior patient outcomes compared to non-model-based allocation methods.

Conclusions:

• Linear programming provides an effective framework for optimizing scarce resource allocation during health crises.
• The developed model can significantly reduce COVID-19 related fatalities.
• This approach offers a valuable tool for evidence-based decision-making in pandemic resource management.