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Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

Authors :
Md. Shahriare Satu
Koushik Chandra Howlader
Mufti Mahmud
M. Shamim Kaiser
Sheikh Mohammad Shariful Islam
Julian M. W. Quinn
Salem A. Alyami
Mohammad Ali Moni
Source :
Applied Sciences, Vol 11, Iss 9, p 4266 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.34f3dadf050f409db0167ed43c90054c
Document Type :
article
Full Text :
https://doi.org/10.3390/app11094266