Practical Multi-Objective Optimization Approaches for Decision Making in Health Care Considering Infectious Disease Dynamics and Uncertainties

Abstract

Operations research methods have been commonly used to inform decisions in health care related to inventory management, policy implementation, and resource allocation. However, the current research does not address many of the unique challenges and objectives faced by decision makers and stakeholders involved in managing infectious diseases. For example, many optimization approaches have a single objective, and do not balance overall health outcomes with health equity metrics. The goal of my dissertation research is to develop practical, effective, and equitable approaches that address challenges in the management of infectious diseases. To achieve this goal, my research combines state-of-the-art mathematical modeling methodologies including optimization, dynamic transmission compartmental modeling, and statistics with the perspectives of several organizations and researchers across multiple disciplines, including epidemiologists, economists, and public health officials. The challenges and objectives addressed in this research include but are not limited to, dynamics and uncertainties in demand, supply, and health outcomes, multiple objectives, and vulnerabilities of populations. This research contributes to three realistic applications, including:• inventory and order management for multiple healthcare commodities and populations during an infectious disease outbreak, and • policy analysis of tuberculosis and HIV health care program interventions in KwaZulu-Natal, South Africa, and • budget allocation to regional tuberculosis and HIV health care programs across the nine provinces of South Africa. The three applications and methodologies provide practical approaches that address these challenges and pave the way for new, more practical, effective, and equitable approaches to decision making in health care for the management of infectious disease.