A multi-objective approach to identify parameters of compartmental epidemiological models—Application to Ebola Virus Disease epidemics

In this work, we propose a novel methodology to identify parameters of compartmental epidemiological models. It is based on solving multi-objective optimization problem that consists in fitting some the model outputs real observations. First, according available data considered epidemic, define where are variables. Then, solved by considering particular algorithm called ParWASF-GA (Parallel Weighting Achievement Scalarizing Function Genetic Algorithm). Finally, decision maker chooses, within set possible solutions, values better suit his/her preferences. order illustrate benefit using our approach, it applied estimate deterministic model, Be-CoDiS (Between-Countries Disease Spread), used forecast spread human diseases and between countries. We consider from different Ebola outbreaks 2014 up 2019. all cases, proposed helps obtain reasonable predictions epidemic magnitudes with model.