Geospatial location-allocation optimization for maximum coverage in pharmacy-based immunization strategies

Abstract

(Only 1 page) Since the beginning of the Coronavirus disease outbreak in 2019, around 184 million cases and 4 million deaths have been reported worldwide [101]. The early decisions made by the entities responsible for managing the health emergency in each country were crucial to define their future. Detection with massive population screening tests, containment with the isolation of suspected and infected cases, and preparation of the healthcare system to face the demand were key factors in controlling the transmission of the disease. Therefore, the primary objective of this thesis is to develop an adaptable integrated geospatial model that ensures efficient distribution of vaccines during health emergencies, illustrating this through a case study in Jalisco, Mexico. In order to complete the aforementioned, a literature review on pharmacy-based im- munization was conducted before developing the mathematical approach. Subsequently, a facility location allocation problem and a hybrid approach known as fix-and-optimize were used to solve larger instances, using the Gurobi optimization software. In addition to contributing to the literature on Humanitarian Logistics in health sys- tems, the results will inform national policymakers at the tactical and strategic decision levels about the development of anticipatory governance for managing resources during situations arising from the Covid-19 pandemic.