Modeling Transmission Rate of COVID-19 in Regional Countries to Forecast Newly Infected Cases in a Nation by the Deep Learning Method

Autor:	Le Duy Dong, Phan Trung Hieu, Dinh Tuan Le, Vu Thanh Hien, Mai Viet Tiep, Phu Phuoc Huy, Vu Thanh Nguyen
Rok vydání:	2021
Předmět:	Coronavirus disease 2019 (COVID-19) Computer science business.industry Term memory Deep learning Transmission rate Machine learning computer.software_genre Recurrent neural network Artificial intelligence business Set (psychology) Hybrid model computer
Zdroj:	Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications ISBN: 9789811680618 FDSE (CCIS Volume)
DOI:	10.1007/978-981-16-8062-5_28
Popis:	This paper presents a deep learning approach to predict new COVID-19 infected cases in a specific country with insufficient data at the onset of the outbreak. We collected data on daily new confirmed cases in several countries of the region where COVID-19 occurred earlier and caused more severe effects than in Vietnam. Then we computed some deep machine learning models to adapt the spreading speed of Delta strain in each nation to generate various scenarios for the epidemic situation in Vietnam. We used models based on recurrent neural networks (RNN) architectures such as long-short term memory (LSTM), gated recurrent unit (GRU), and several hybrid structures between LSTM and GRU. Learning from the experiments in this research, we built a set of circumstances for COVID-19 in Vietnam. We also found that GRU always gives the best performance in terms of MSE, while LSTM is the worst.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b309cb17e059764637329048599de99f https://doi.org/10.1007/978-981-16-8062-5_28 Zobrazit plný text záznamu