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376. Influence of Inventory Policies on Epidemic Progression in Networked Metapopulations

Invited abstract in session WB-21: Infectious diseases and pandemics, stream OR in Humanitarian Operations (HOpe).

Wednesday, 10:30-12:00
Room: 49 (building: 116)

Authors (first author is the speaker)

1. Laura Heuser
ETH Zurich
2. Stephan Wagner
Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich (ETH Zurich)

Abstract

Infectious diseases cause more than 25% of global deaths, but appropriate treatment can reduce the impact of epidemics. To account for treatment availability, we develop a networked metapopulation Susceptible-Exposed-Infected-Treatment-Recovered (SEITR) model that depends on supply chain inventory policies. The focus of this paper is on inventory policies during preparation (Prepositioning) and response (Reorder Point, Ordering, Inventory Rationing, and Delivery Scheduling) to the epidemic. Our approach involves Python-based simulations with customized Latin Hypercube sampling to sample parameter-policy combinations and Monte Carlo techniques to account for randomness in the model. Using the case of Uganda's three-tier medical supply chain, we analyze the impact of different parameter-policy combinations on epidemic outbreaks. The analysis quantifies this impact through key performance indicators (KPIs) from epidemics and supply chains, including final epidemic size, treatment ratio, outbreak duration, stockouts, service levels, and inventory left over. We will identify optimal policy-parameter combinations that improve responses to future epidemic outbreaks within specific categories of epidemics. We aim to provide actionable insights to enhance Uganda's national inventory policy for public health emergencies while contributing to the literature by providing insights about the most influential policies and parameters.

Keywords

Status: accepted

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