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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: May 6, 2020
Date Accepted: Sep 1, 2020
Date Submitted to PubMed: Sep 2, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Jung SY, Jo H, Son H, Hwang HJ

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

J Med Internet Res 2020;22(9):e19907

DOI: 10.2196/19907

PMID: 32877350

PMCID: 7486001

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

  • Se Young Jung; 
  • Hyeontae Jo; 
  • Hwijae Son; 
  • Hyung Ju Hwang

ABSTRACT

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).

Results:

We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional 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.

Conclusions:

The methodology and new model of this study could be employed for proactive intervention. 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.


 Citation

Please cite as:

Jung SY, Jo H, Son H, Hwang HJ

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

J Med Internet Res 2020;22(9):e19907

DOI: 10.2196/19907

PMID: 32877350

PMCID: 7486001

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