Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation

Abstract

Background The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. Objective The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and 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 the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) 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 fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. Conclusions The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare 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.

What Can COVID-19 Teach Us about Using AI in Pandemics?

“…Soon, other AI engines were forecasting the dynamics of outbreaks in Saudi Arabia, Egypt, Brazil, Canada, India, USA, and African countries [ 26 , 33 , 34 , 35 , 36 ]. The most popular approach in AI design incorporates a long-term short memory-based AI engine utilizing rolling training sets [ 26 , 33 , 37 , 38 , 39 ]. Others used advanced autoregressive integrated moving average [ 18 , 35 , 40 ].…”

“…It remains to be seen which of these AI engines perform with higher sensitivity. The data sources could be numerous and include governmental data, social media, and collateral data from mobile devices or public surveillance systems [ 17 , 37 , 38 , 41 ]. The relative scarcity of data for training constrains but does not prohibit AI development and deployment even when resources are limited [ 42 ].…”

“…Refined analysis of poorly refined, incomplete, or biased data Abdication of human responsibility for triage decision-making [13][14][15][16][17][18]20,25,26,37,[69][70][71][72] How might AI accelerate development of medical therapies and treatment protocols?…”

A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics

“…Umar et al [34] presented a SITR model representing the dynamics of COVID-19 and then used the modern stochastic intelligent computational methodology based on feed forward artificial neural networks to solve that model to study the variation of various classes on different involved parameters. Jung et al [35] proposed a SIR model based on different classes to present the dynamics of COVID-19 in South Korea and then used neural network with deep learning to solve the model. Naik et al [36] numerically investigated a COVID-19 model based on Caputo operator and presented graphical results to envision the effectiveness of introduced arbitrary order derivative.…”

Dashboard of Sentiment in Austrian Social Media During COVID-19

“…Interactive online dashboards are an accessible way to summarize complex information to the public. During COVID-19, popular dashboards have conveyed information about the evolution of the number of COVID-19 cases in different regions of Austria (Austrian Ministry for Health, 2020 ) and globally (CSSE, 2020 ). Other dashboards track valuable information such as world-wide COVID-19 registry studies (Thorlund et al, 2020 ).…”