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    Forecast of COVID-19 New Cases Using an MLP Regressor

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    Final Report CS598-AlarconH-Gr ...
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    Author
    Alarcon, Hector
    Keyword
    COVID-19
    Prediction methodologies
    Artificial neural networks (ANN)
    Readers/Advisors
    Hashem, Sherif
    Andriamanalimanana, Bruno
    Sengupta, Saumendra
    Carpenter, Michael
    Term and Year
    Fall 2022
    Date Published
    2022-12
    
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    URI
    http://hdl.handle.net/20.500.12648/8378
    Abstract
    Over the past year the COVID-19 pandemic has overwhelmed healthcare systems and government institutions worldwide, creating the need for accurate prediction of confirmed cases. Current research focuses on prediction methodologies aiming at mitigating economical anxiety and aiding in detection, preemption and forecasting of the pandemic. Modeling using artificial neural networks (ANN) is an important component of this effort and this research aims to contribute with a methodology to forecast occurrence of new cases of COVID-19 1, 2 and 7 days ahead by using historical case data. These approaches were compared and contrasted with some published results in terms of network architecture and neural network of use. Three regressors were developed and showcased in this document with their accuracy evaluated using the root mean squared error (RMSE) from 1179 to 2806. The quality evaluation supports the conclusion that these regressors compete with those of current architectures.
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    SUNY Polytechnic Institute College of Engineering

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