Vaccination Planning in Peru using Constraint Programming

Ugarte W. · International Conference on Agents and Artificial Intelligence (2022) · DOI: 10.5220/0010899700003116

Resumen

Vaccination has been proven to be the most effective method to prevent infectious diseases, specially nowadays with the global pandemic of CoViD19. Millions of people are not immunized yet in various countries because of low vaccine availability resulting from inefficiencies and/or lack of access to the vaccines. We propose a constraint programming model, kwnown as Constraint Satisfaction Problem (CSP) as a distribution model for vaccination to address the unique characteristics and challenges facing vaccine dose assignation. This CSP model capture the uncertainties of demand for vaccinations such as the age range of the vaccination campaign and the location of vaccination centers. The objective is to maximize the percentage of fully immunized people facilitating the access by location and capacity of the vaccination centers while respecting the health ministry dispositions (e.g., age range, number of doses, etc.). Our research examines how these can be optimized with a constraint optimization problem in a single objective function. We tested the model using Peru open data on vaccination planning of their national health ministry. We make many experiments to show the feasibility of our proposal to increase their immunization coverage.

Instituciones: Universidad Peruana de Ciencias Aplicadas