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Adversarial Training for Predicting the Trend of the COVID-19 Pandemic
Journal of Database Management ; 33(1), 2022.
Article in English | Web of Science | ID: covidwho-2201333
ABSTRACT
It is significant to accurately predict the epidemic trend of COVID-19 due to its detrimental impact on the global health and economy. Although machine learning-based approaches have been applied to predict epidemic trend, standard models have shown low accuracy for long-term prediction due to a high level of uncertainty and lack of essential training data. This paper proposes an improved machine learning framework employing generative adversarial network (GAN) and long short-term memory (LSTM) for adversarial training to forecast the potential threat of COVID-19 in countries where COVID-19 is rapidly spreading. It also investigates the most updated COVID-19 epidemiological data before October 18, 2020 and models the epidemic trend as time series that can be fed into the proposed model for data augmentation and trend prediction of the epidemic. The model is trained to predict daily numbers of cumulative confirmed cases of COVID-19 in Italy, USA, China, Germany, UK, and across the world. The paper further analyzes and suggests which populations are at risk of contracting COVID-19.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Journal of Database Management Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: Journal of Database Management Year: 2022 Document Type: Article