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Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is pub...

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Autores principales: Khan, Riaz Ullah, Almakdi, Sultan, Alshehri, Mohammed, Kumar, Rajesh, Ali, Ikram, Hussain, Sardar Muhammad, Haq, Amin Ul, Khan, Inayat, Ullah, Aman, Uddin, Muhammad Irfan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600455/
https://www.ncbi.nlm.nih.gov/pubmed/36292228
http://dx.doi.org/10.3390/diagnostics12102539
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author Khan, Riaz Ullah
Almakdi, Sultan
Alshehri, Mohammed
Kumar, Rajesh
Ali, Ikram
Hussain, Sardar Muhammad
Haq, Amin Ul
Khan, Inayat
Ullah, Aman
Uddin, Muhammad Irfan
author_facet Khan, Riaz Ullah
Almakdi, Sultan
Alshehri, Mohammed
Kumar, Rajesh
Ali, Ikram
Hussain, Sardar Muhammad
Haq, Amin Ul
Khan, Inayat
Ullah, Aman
Uddin, Muhammad Irfan
author_sort Khan, Riaz Ullah
collection PubMed
description The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach’s alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks.
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spelling pubmed-96004552022-10-27 Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network Khan, Riaz Ullah Almakdi, Sultan Alshehri, Mohammed Kumar, Rajesh Ali, Ikram Hussain, Sardar Muhammad Haq, Amin Ul Khan, Inayat Ullah, Aman Uddin, Muhammad Irfan Diagnostics (Basel) Article The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach’s alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks. MDPI 2022-10-19 /pmc/articles/PMC9600455/ /pubmed/36292228 http://dx.doi.org/10.3390/diagnostics12102539 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
Khan, Riaz Ullah
Almakdi, Sultan
Alshehri, Mohammed
Kumar, Rajesh
Ali, Ikram
Hussain, Sardar Muhammad
Haq, Amin Ul
Khan, Inayat
Ullah, Aman
Uddin, Muhammad Irfan
Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network
title Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network
title_full Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network
title_fullStr Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network
title_full_unstemmed Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network
title_short Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network
title_sort probabilistic approach to covid-19 data analysis and forecasting future outbreaks using a multi-layer perceptron neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600455/
https://www.ncbi.nlm.nih.gov/pubmed/36292228
http://dx.doi.org/10.3390/diagnostics12102539
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