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Artificial Neural Networks for the Prediction of Monkeypox Outbreak
While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from m...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783768/ https://www.ncbi.nlm.nih.gov/pubmed/36548679 http://dx.doi.org/10.3390/tropicalmed7120424 |
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author | Manohar, Balakrishnama Das, Raja |
author_facet | Manohar, Balakrishnama Das, Raja |
author_sort | Manohar, Balakrishnama |
collection | PubMed |
description | While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from many countries. In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the monkeypox outbreak to and throughout the USA, Germany, the UK, France and Canada. We have used certain effective neural network models for this purpose. The novelty of this study is that a neural network model for a time series monkeypox dataset is developed and compared with LSTM and GRU models using an adaptive moment estimation (ADAM) optimizer. The Levenberg–Marquardt (LM) learning technique is used to develop and validate a single hidden layer artificial neural network (ANN) model. Different ANN model architectures with varying numbers of hidden layer neurons were trained, and the K-fold cross-validation early stopping validation approach was employed to identify the optimum structure with the best generalization potential. In the regression analysis, our ANN model gives a good R-value of almost 99%, the LSTM model gives almost 98% and the GRU model gives almost 98%. These three model fits demonstrated that there was a good agreement between the experimental data and the forecasted values. The results of our experiments show that the ANN model performs better than the other methods on the collected monkeypox dataset in all five countries. To the best of the authors’ knowledge, this is the first report that has used ANN, LSTM and GRU to predict a monkeypox outbreak in all five countries. |
format | Online Article Text |
id | pubmed-9783768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97837682022-12-24 Artificial Neural Networks for the Prediction of Monkeypox Outbreak Manohar, Balakrishnama Das, Raja Trop Med Infect Dis Article While the world is still struggling to recover from the harm caused by the widespread COVID-19 pandemic, the monkeypox virus now poses a new threat of becoming a pandemic. Although it is not as dangerous or infectious as COVID-19, new cases of the disease are nevertheless being reported daily from many countries. In this study, we have used public datasets provided by the European Centre for Disease Prevention and Control for developing a prediction model for the spread of the monkeypox outbreak to and throughout the USA, Germany, the UK, France and Canada. We have used certain effective neural network models for this purpose. The novelty of this study is that a neural network model for a time series monkeypox dataset is developed and compared with LSTM and GRU models using an adaptive moment estimation (ADAM) optimizer. The Levenberg–Marquardt (LM) learning technique is used to develop and validate a single hidden layer artificial neural network (ANN) model. Different ANN model architectures with varying numbers of hidden layer neurons were trained, and the K-fold cross-validation early stopping validation approach was employed to identify the optimum structure with the best generalization potential. In the regression analysis, our ANN model gives a good R-value of almost 99%, the LSTM model gives almost 98% and the GRU model gives almost 98%. These three model fits demonstrated that there was a good agreement between the experimental data and the forecasted values. The results of our experiments show that the ANN model performs better than the other methods on the collected monkeypox dataset in all five countries. To the best of the authors’ knowledge, this is the first report that has used ANN, LSTM and GRU to predict a monkeypox outbreak in all five countries. MDPI 2022-12-08 /pmc/articles/PMC9783768/ /pubmed/36548679 http://dx.doi.org/10.3390/tropicalmed7120424 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Manohar, Balakrishnama Das, Raja Artificial Neural Networks for the Prediction of Monkeypox Outbreak |
title | Artificial Neural Networks for the Prediction of Monkeypox Outbreak |
title_full | Artificial Neural Networks for the Prediction of Monkeypox Outbreak |
title_fullStr | Artificial Neural Networks for the Prediction of Monkeypox Outbreak |
title_full_unstemmed | Artificial Neural Networks for the Prediction of Monkeypox Outbreak |
title_short | Artificial Neural Networks for the Prediction of Monkeypox Outbreak |
title_sort | artificial neural networks for the prediction of monkeypox outbreak |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783768/ https://www.ncbi.nlm.nih.gov/pubmed/36548679 http://dx.doi.org/10.3390/tropicalmed7120424 |
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