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

Journal: Journal of medical Internet research

Published Date: Sep 9, 2020

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.

Authors

Se Young Jung

Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Hyeontae Jo

Basic Science Research Institute, Pohang University of Science and Technology, Pohang, Republic of Korea.
Hwijae Son

Department of Mathematics, Pohang University of Science and Technology, Pohang, Republic of Korea.
Hyung Ju Hwang

Department of Mathematics, Pohang University of Science and Technology, Pohang, Republic of Korea.

Keywords

Betacoronavirus Coronavirus Infections COVID-19 Deep Learning Humans Mass Media Models, Theoretical Neural Networks, Computer Pandemics Pneumonia, Viral Republic of Korea SARS-CoV-2 Time Factors

External Resources

View on PubMed Access via DOI PubMed (32877350)

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

Abstract

Authors

Keywords

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