Artificial Intelligence Prediction Model For Forecasting COVID-19

Artificial Intelligence Prediction Model For Forecasting COVID-19 | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 January 2025 V1 Latest version Share on Artificial Intelligence Prediction Model For Forecasting COVID-19 Authors : Fadhil Mukhlif 0000-0002-1064-7292 [email protected] , Norafida Ithnin , and Ibrahim Hashem Authors Info & Affiliations https://doi.org/10.22541/au.173801006.66052508/v1 514 views 167 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The World Health Organization (WHO) refers to the 2019 new coronavirus epidemic as COVID-19, and it has caused an unprecedented global crisis for several nations. Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks, which were previously only experienced by Chinese residents. Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems. Every time the pandemic surprises them by providing new values for various parameters, all the connected research groups strive to understand the behavior of the pandemic to determine when it will stop. As a result, the incidence of the COVID-19 outbreak in Malaysia and the United Arab Emirates is predicted in this study using artificial intelligence-based models. These models comprise extended short-term memory networks (LSTM) and bidirectional (biLSTM). Utilizing the determination coefficient and root means square error, the outputs of the models are evaluated. When predicting the cumulative infections for the next week and month, the LSTM model performs best. Furthermore, 90% and 10% of the data reported by the Malaysian and UAE ministries of health and Population between January 03, 2020, and July 05, 2023, respectively, were used to train and test the models, the LSTM model is applied to forecast the spread of this disease for the next month. The result of this study may help policymakers to implement measures to combat any COVID-19 variation. Supplementary Material File (artificial intelligence prediction model for forecasting covid-19.pdf) Download 937.77 KB Information & Authors Information Version history V1 Version 1 27 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ai artificial intelligence bilstm covid-19 deep learning healthcare engineer lstm machine learning rnn Authors Affiliations Fadhil Mukhlif 0000-0002-1064-7292 [email protected] Computer Engineering Techniques Department, Cybersecurity and Artificial Intelligence Research Group (CAIRG), Technical Engineering College, Northern Technical University View all articles by this author Norafida Ithnin Faculty of Computing, Information Assurance and Security Research Group (IASRG), Universiti Teknologi Malaysia (UTM) View all articles by this author Ibrahim Hashem College of Computing and Informatics, Department of Computer Science, University of Sharjah View all articles by this author Metrics & Citations Metrics Article Usage 514 views 167 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Fadhil Mukhlif, Norafida Ithnin, Ibrahim Hashem. Artificial Intelligence Prediction Model For Forecasting COVID-19. Authorea . 27 January 2025. DOI: https://doi.org/10.22541/au.173801006.66052508/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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