Whenever new viruses came into being, mankind has acquired various knowledge including their nature and transmission, and the symptoms and treatment of the diseases, and made vaccines and established treatment tactics.
One of the important issues to cope with infectious diseases is prediction of virus transmission by mathematical modeling. Based on mathematical modeling, it has been widely studied in the world. Mathematical modeling for predicting future epidemics makes it possible to predict the number of medical personnel and beds, medical equipment, medical drug supplies, etc. that are needed to prepare at hospitals. It also helps find a suitable approach for medical service through simulations.
The parameters included in this mathematical model are the key elements that characterize the epidemic of diseases. Therefore, it is important to set the parameters realistically.
Kim Ju Hyong, a researcher at the Faculty of Biomedical Engineering, proposed a method for determining the parameters involved in mathematical models of epidemics using genetic algorithms, and verified its validity.
To test the validity of this method, he built a SEIHRD model and compared the accuracy of the simulation with unknown parameters determined when the underlying data were prepared in various forms.
The results show that the proposed method is a recursive one for simply determining unknown parameters in a mathematical model study considering the spread of epidemics.
For more information, please refer to his paper “A method to determine the unknown parameters of mathematical model of epidemic transmission by genetic algorithm” in “Network Modeling Analysis in Health Informatics and Bioinformatics” (SCI).