Fair and Efficient Allocation and Distribution of Vaccines During a Pandemic

Sibel Salman, Department of Industrial Engineering, Koç University

Abstract

In 2020 and 2021, the emergence of COVID-19 gave rise to an unprecedented global health crisis. Globally, more than 576 million cases and 6 million deaths from COVID-19 have been reported. Widespread vaccination became instrumental in controlling the spread of the disease. In this talk two studies related to vaccination will be presented. In the first study, we focus on the vaccine allocation problem (VAP). Given a set of locations with initial populations of susceptible, vaccinated and infected individuals, as well as hospital capacities and infection parameters, the VAP aims to determine the optimum allocation of the vaccine among the locations within a fixed planning horizon, subject to production capacity and with the objective of minimizing total mortality and inequity over the locations. The spread of the pandemic is modelled using the well-established Susceptible–Infected–Recovered (SIR) epidemiological model, extended with the vaccination status, and integrated into the vaccine allocation decisions. We will present a nonlinear mixed integer programming (MIP) model which has been solved optimally for reasonable-sized instances generated based on the UK pandemic data. Our analysis shows that the impact rate dimension, which includes the death rate, hospitalization rate, and recovery rate, has a significant impact on the outcomes of the pandemic. In the second part of the talk, we will focus on people that have difficulty accessing vaccination services, such as those living in rural areas, disabled or elderly people, and refugees, for which it is observed that vaccination rates are lower. We optimize the logistics of delivering vaccination services with mobile facilities that get closer to the locations of such people by an MIP model that determines the routes of the mobile facilities, and the vaccine amounts to be administered at different locations over a multi-period planning horizon with several service level, cost and equity objectives. To solve large instances, we follow a hierarchical matheuristic. We test our methods in the case of COVID-19 vaccination of Syrian refugees and Turkish citizens living in different neighborhoods of Gaziantep and derive some insights.

Sibel Salman is a Professor at the Industrial Engineering Department of the College of Engineering at Koç University in Istanbul, Turkey. Prior to joining Koç University, she held a faculty position at the Krannert School of Management, Purdue University, USA. She got her Ph.D. in Operations Research from Carnegie Mellon University, the USA, and her M.Sc. and B.Sc. degrees from Bilkent University, Turkey. She has published in the areas of disaster and health care logistics, supply chain management, facility location, vehicle routing, network design, production scheduling, approximation algorithms, and combinatorial optimization. She is a department editor for the OR Spectrum, an associate editor for the Sustainable Analytics and Modeling and Socio-Economic Planning Science journals and has been/is on the editorial board of the European Journal of Operational Research, Computers and Operations Research, and Production and Operations Management journals. She is a co-founder of the EURO working group on Humanitarian Operations (HOpe) and is currently a coordinating member of the EURO WISDOM Forum. She is a member of the Science Academy in Turkey. Her current research interests are in humanitarian logistics, disaster management, healthcare logistics, and e- commerce last-mile delivery.

Venue

Friday, June 9, 2023, 4.00 pm - Zoom