Short-term prediction of covid-19 cases using machine learning models
Satu, MS, Howlader, KC, Mahmud, M, Shamim Kaiser, M, Islam, Shariful, Quinn, JMW, Alyami, SA and Moni, MA 2021, Short-term prediction of covid-19 cases using machine learning models, Applied Sciences, vol. 11, no. 9, pp. 4266-4283, doi: 10.3390/app11094266.
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Short-term prediction of covid-19 cases using machine learning models
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.
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.
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