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Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model

Although more than a year has passed since the coronavirus outbreak globally, the Covid-19 pandemic conditions still exist in many countries, including Iran. Predicting the number of future patients and deaths can help governments and policymakers make better decisions to enforce disease control res...

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Autor principal: Haghighat, Fatemeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416568/
https://www.ncbi.nlm.nih.gov/pubmed/34511743
http://dx.doi.org/10.1016/j.chaos.2021.111399
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author Haghighat, Fatemeh
author_facet Haghighat, Fatemeh
author_sort Haghighat, Fatemeh
collection PubMed
description Although more than a year has passed since the coronavirus outbreak globally, the Covid-19 pandemic conditions still exist in many countries, including Iran. Predicting the number of future patients and deaths can help governments and policymakers make better decisions to enforce disease control restrictions. In this study, we aim to use a combined multilayer perceptron (MLP) neural network and Markov chain (MC) model to predict two indicators of the number of discharged and death cases according to their relationship with the number of hospitalized cases in Bushehr province, Iran. This hybrid model is called MLP-MC. In this study, 136 data (days) are collected from May 13, 2020, to April 1, 2021, divided into two parts: training and test. The training data are used to train the MLP network, and the trained MLP network is used to predict the test data and the next 40 days. Then the residual errors of actual and predicted values are calculated. In the next step, the MC model is used to classify the errors and predict the values of the indicators according to the probabilities related to the error states and improve the performance of the MLP model in forecasting. Finally, the prediction accuracy of MLP and MLP-MC models are compared using three evaluation metrics: MAD, MSE and RMSE. This comparison showed that the MLP-MC model has slightly higher prediction accuracy than the MLP model.
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spelling pubmed-84165682021-09-07 Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model Haghighat, Fatemeh Chaos Solitons Fractals Article Although more than a year has passed since the coronavirus outbreak globally, the Covid-19 pandemic conditions still exist in many countries, including Iran. Predicting the number of future patients and deaths can help governments and policymakers make better decisions to enforce disease control restrictions. In this study, we aim to use a combined multilayer perceptron (MLP) neural network and Markov chain (MC) model to predict two indicators of the number of discharged and death cases according to their relationship with the number of hospitalized cases in Bushehr province, Iran. This hybrid model is called MLP-MC. In this study, 136 data (days) are collected from May 13, 2020, to April 1, 2021, divided into two parts: training and test. The training data are used to train the MLP network, and the trained MLP network is used to predict the test data and the next 40 days. Then the residual errors of actual and predicted values are calculated. In the next step, the MC model is used to classify the errors and predict the values of the indicators according to the probabilities related to the error states and improve the performance of the MLP model in forecasting. Finally, the prediction accuracy of MLP and MLP-MC models are compared using three evaluation metrics: MAD, MSE and RMSE. This comparison showed that the MLP-MC model has slightly higher prediction accuracy than the MLP model. Elsevier Ltd. 2021-11 2021-09-04 /pmc/articles/PMC8416568/ /pubmed/34511743 http://dx.doi.org/10.1016/j.chaos.2021.111399 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Haghighat, Fatemeh
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model
title Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model
title_full Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model
title_fullStr Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model
title_full_unstemmed Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model
title_short Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model
title_sort predicting the trend of indicators related to covid-19 using the combined mlp-mc model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416568/
https://www.ncbi.nlm.nih.gov/pubmed/34511743
http://dx.doi.org/10.1016/j.chaos.2021.111399
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