ArticleInternational journal of environmental research and public health2021
Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.BioMed research international · 2022Pooled it
- Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism.Scientific reports · 2025Article
- Leveraging mHealth Technologies for Public Health.JMIR public health and surveillance · 2024Review
- Effect of Inhaled Ciclesonide in Non-Critically Ill Hospitalized Patients With Coronavirus Disease 2019: A Multicenter Observational Study in Japan.Open forum infectious diseases · 2023Article
- Projection of COVID-19 Positive Cases Considering Hybrid Immunity: Case Study in Tokyo.Vaccines · 2023Article
- The Effects of Time Window-Averaged Mobility on Effective Reproduction Number of COVID-19 Viral Variants in Urban Cities.Journal of urban health : bulletin of the New York Academy of Medicine · 2023Article
- COVID-19 forecasting using new viral variants and vaccination effectiveness models.Computers in biology and medicine · 2022Article
- Water Quality Prediction Based on Multi-Task Learning.International journal of environmental research and public health · 2022Article
- Did the Tokyo Olympic Games enhance the transmission of COVID-19? An interpretation with machine learning.Computers in biology and medicine · 2022Article
- Dynamic Prediction of Non-Neutral SARS-Cov-2 Variants Using Incremental Machine Learning.Studies in health technology and informatics · 2022Article
- 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".International journal of environmental research and public health · 2022Article
- Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application "MorChana" in Thailand: UTAUT2 Approach.International journal of environmental research and public health · 2022Article
- Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application "Thai Chana" during the Early COVID-19 Omicron Period.International journal of environmental research and public health · 2022Article
- Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics.Network modeling and analysis in health informatics and bioinformatics · 2022Review
- Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics.Journal of personalized medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.