Simulation of Influenza Pandemic Based on Genetic Algorithm and Agent-Based Modeling: A Multi-objective Optimization Problem Solving
Abstract: This paper describes
the analysis, design and development process of simulation software for the
Avian Influenza (H5N1) viruses mutation. Influenza Pandemics, which have
occurred since 1729, caused by mutation (antigenic drift) and recombination
(antigenic shift) of Influenza viruses. The
purpose of this research is to define the modeling of virus mutation causing
the Influenza Pandemic phenomena. Additionally, the objective of this
simulation is to obtain all possible virus strains might be formed from
mutation, the scope within this article, which can potentially trigger
Influenza Pandemic. These new strains could then be utilized to support the vaccine
planning process. The Influenza Pandemic
simulation program can be developed based on Genetic Algorithm method, for
solving this multi-objective optimization problem. By utilizing the Genetic
Algorithm approach, the chromosome solution and fitness values/functions of
Influenza Pandemic stages are defined and the maximum fitness values are to be searched.
The simulation result of H5N1 mutation gave
3 (three) best fitness values and a more dynamic mean fitness values,
including best fitness value from several mutations combination. Simulation
program was developed by utilizing MATLAB© software, with Genetic Algorithm
Toolbox provided.
Keywords: Avian Influenza,
Influenza Pandemic, Mutation, Multi-objective Optimization, Genetic Algorithm,
Agent-based modeling
Author: Ria Lestari Moedomo,
Adi Pancoro, Jorga Ibrahim, Adang Suwandi Ahmad, Muhammad Sukrisno Mardiyanto,
Mohammad Bahrelfi Belatiff, and Hengki Tasman
Journal Code: jpkeperawatangg100001