2026-06-26T19:49:20Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/724352025-02-17T13:02:22Zcom_20.500.14352_14col_20.500.14352_15

Ferrández, M.R. Ivorra, Benjamín Pierre Paul Redondo, Juana L. Ramos Del Olmo, Ángel Manuel Ortigosa, Pilar M. 2023-06-22T12:26:21Z 2023-06-22T12:26:21Z 2023 1007-5704 10.1016/j.cnsns.2023.107165 https://hdl.handle.net/20.500.14352/72435 https://doi.org/10.1016/j.cnsns.2023.107165 https://www.sciencedirect.com/science/article/pii/S1007570423000837 In this work, we propose a novel methodology to identify parameters of compartmental epidemiological models. It is based on solving a multi-objective optimization problem that consists in fitting some of the model outputs to real observations. First, according to the available data of the considered epidemic, we define a multi-objective optimization problem where the model parameters are the optimization variables. Then, this problem is solved by considering a particular optimization algorithm called ParWASF-GA (Parallel Weighting Achievement Scalarizing Function Genetic Algorithm). Finally, the decision maker chooses, within the set of possible solutions, the values of parameters that better suit his/her preferences. In order to illustrate the benefit of using our approach, it is applied to estimate the parameters of a deterministic epidemiological model, called Be-CoDiS (Between-Countries Disease Spread), used to forecast the possible spread of human diseases within and between countries. We consider data from different Ebola outbreaks from 2014 up to 2019. In all cases, the proposed methodology helps to obtain reasonable predictions of the epidemic magnitudes with the considered model. eng https://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España A multi-objective approach to identify parameters of compartmental epidemiological models—Application to Ebola Virus Disease epidemics journal article