COVID19 – Mathematical Modeling and Machine Learning

Abstract

This presentation include the following two topics. First of all, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional model to confirm its convergent nature. Next, We also developed a machine learning model that predicts the mortality of infected patients by using basic patients information such as age, residence, comorbidity, and past medical history. Furthermore, we aim to establish a medical system that allows patients to check their own severity, and informs them to visit the appropriate clinic center by referring to the past treatment details of other patients with similar severity.