In Journal of medical Internet research ; h5-index 88.0
BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has been a major shock to the whole world since March 2020. From the experience of the 1918 influenza pandemic, we know that decreases in infection rates of COVID-19 do not guarantee continuity of the trend.
OBJECTIVE : This study was conducted to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning responding promptly to the dynamic situation of the outbreak to proactively minimize damage.
METHODS : In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from Korea Centers for Disease Control & Prevention (KCDC) and Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Since the data consist of confirmed, recovered, and deceased cases, we selected the SIR (Susceptible - Infected - Recovered) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters, and designed suitable loss functions.
RESULTS : We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta of order 4 (RK4) model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from KCDC, the Korean government, and news media. We also cross-validated our model by using data from CSSE for Italy, Sweden, and US.
CONCLUSIONS : The methodology and new model of this study could be employed for short term prediction of COVID-19, by which the government can be prepared for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.
Jung Se Young, Jo Hyeontae, Son Hwijae, Hwang Hyung Ju