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Article

Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling

by 1,2,* and 1,3
1
Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
2
Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt
3
Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: José-Victor Rodríguez, Andrés Ortiz García and Ignacio Rodríguez-Rodríguez
Int. J. Environ. Res. Public Health 2021, 18(15), 7799; https://doi.org/10.3390/ijerph18157799
Received: 21 June 2021 / Revised: 12 July 2021 / Accepted: 20 July 2021 / Published: 22 July 2021
The significant health and economic effects of COVID-19 emphasize the requirement for reliable forecasting models to avoid the sudden collapse of healthcare facilities with overloaded hospitals. Several forecasting models have been developed based on the data acquired within the early stages of the virus spread. However, with the recent emergence of new virus variants, it is unclear how the new strains could influence the efficiency of forecasting using models adopted using earlier data. In this study, we analyzed daily positive cases (DPC) data using a machine learning model to understand the effect of new viral variants on morbidity rates. A deep learning model that considers several environmental and mobility factors was used to forecast DPC in six districts of Japan. From machine learning predictions with training data since the early days of COVID-19, high-quality estimation has been achieved for data obtained earlier than March 2021. However, a significant upsurge was observed in some districts after the discovery of the new COVID-19 variant B.1.1.7 (Alpha). An average increase of 20–40% in DPC was observed after the emergence of the Alpha variant and an increase of up to 20% has been recognized in the effective reproduction number. Approximately four weeks was needed for the machine learning model to adjust the forecasting error caused by the new variants. The comparison between machine-learning predictions and reported values demonstrated that the emergence of new virus variants should be considered within COVID-19 forecasting models. This study presents an easy yet efficient way to quantify the change caused by new viral variants with potential usefulness for global data analysis. View Full-Text
Keywords: COVID-19; forecasting; deep learning; viral variants COVID-19; forecasting; deep learning; viral variants
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MDPI and ACS Style

Rashed, E.A.; Hirata, A. Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling. Int. J. Environ. Res. Public Health 2021, 18, 7799. https://doi.org/10.3390/ijerph18157799

AMA Style

Rashed EA, Hirata A. Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling. International Journal of Environmental Research and Public Health. 2021; 18(15):7799. https://doi.org/10.3390/ijerph18157799

Chicago/Turabian Style

Rashed, Essam A., and Akimasa Hirata. 2021. "Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling" International Journal of Environmental Research and Public Health 18, no. 15: 7799. https://doi.org/10.3390/ijerph18157799

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