Epidemiological forecasting of COVID-19 infection using deep learning approach
21st IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE)
; 2021.
Article
in English
| Web of Science | ID: covidwho-1764809
ABSTRACT
Since the novel SARS-CoV-2 virus appeared, interest in developing epidemiological mechanisms that would help in prevention of its spread has increased. Epidemiological models are the most important mechanisms for examining the spread of the virus. For that purpose, we propose deep learning approach, LSTM neural network model. LSTM is a special kind of neural network structure capable of learning long-term dependencies in sequence prediction problems. The model was fed with official statistical data available online for Belgium in the period of March 15th, 2020 to March 15th, 2021. Results show that LSTM is capable of predicting in long-term manner with the low values of RMSE and MAE. Higher values of RMSE and MAE are observed in the infected cases (RMSE was 397.23 and MAE was 315.35) which is expected due to thousands of infected people per day in Belgium. In future studies, we will include more phenomena, especially medical intervention and asymptomatic infection, in order to better describe the COVID-19 spread and development.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Observational study
Language:
English
Journal:
21st IEEE International Conference on Bioinformatics and Bioengineering (IEEE BIBE)
Year:
2021
Document Type:
Article
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