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

Author

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  • Essam A. Rashed

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
    Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt)

  • Akimasa Hirata

    (Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
    Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan)

Abstract

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.

Suggested Citation

  • Essam A. Rashed & Akimasa Hirata, 2021. "Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling," IJERPH, MDPI, vol. 18(15), pages 1-15, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:7799-:d:599638
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    References listed on IDEAS

    as
    1. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Ozili, Peterson & Arun, Thankom, 2020. "Spillover of COVID-19: Impact on the Global Economy," MPRA Paper 99317, University Library of Munich, Germany.
    3. Sachiko Kodera & Essam A. Rashed & Akimasa Hirata, 2020. "Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity," IJERPH, MDPI, vol. 17(15), pages 1-14, July.
    4. Essam A. Rashed & Sachiko Kodera & Jose Gomez-Tames & Akimasa Hirata, 2020. "Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-Prefecture Study in Japan," IJERPH, MDPI, vol. 17(15), pages 1-14, July.
    5. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
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    3. Thanatorn Chuenyindee & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Satria Fadil Persada & Reny Nadlifatin & Thaninrat Sittiwatethanasiri, 2022. "Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period," IJERPH, MDPI, vol. 19(7), pages 1-16, April.
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    5. Ardvin Kester S. Ong & Thanatorn Chuenyindee & Yogi Tri Prasetyo & Reny Nadlifatin & Satria Fadil Persada & Ma. Janice J. Gumasing & Josephine D. German & Kirstien Paola E. Robas & Michael N. Young & , 2022. "Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”," IJERPH, MDPI, vol. 19(10), pages 1-24, May.

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