Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation

Soon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testi...

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Autores principales:	Kamal Pasha Mustafa, Gardazi Syed Fasih Ali, Imtiaz Fariha, Qureshi Asma Talib, Afrasiab Rabia
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	covid-19 covid-19 testing binary search methods machine learning artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/0640f6590ead4984aee50070e5fbd922
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spelling	oai:doaj.org-article:0640f6590ead4984aee50070e5fbd9222021-12-05T14:10:51ZIdentification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation2191-026X10.1515/jisys-2021-0041https://doaj.org/article/0640f6590ead4984aee50070e5fbd9222021-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0041https://doaj.org/toc/2191-026XSoon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testing kits for COVID-19 were manufactured for efficiently detecting COVID-19. However, due to limited resources in the densely populated countries, testing capacity even after a year is still a limiting factor for COVID-19 diagnosis on a larger scale and contributes to a lag in disease tracking and containment. Due to this reason, we started this study to provide a better cost-effective solution for enhancing the testing capacity so that the maximum number of people could get tested for COVID-19. For this purpose, we utilized the approach of artificial neural networks (ANN) to acquire the relevant data on COVID-19 and its testing. The data were analyzed by using Machine Learning, and probabilistic algorithms were applied to obtain a statistically proven solution for COVID-19 testing. The results obtained through ANN indicated that sample pooling is not only an effective way but also regarded as a “Gold standard” for testing samples when the prevalence of the disease is low in the population and the chances of getting a positive result are less. We further demonstrated through algorithms that pooling samples from 16 individuals is better than pooling samples of 8 individuals when there is a high likelihood of getting negative test results. These findings provide ground to the fact that if sample pooling will be employed on a larger scale, testing capacity will be considerably increased within limited available resources without compromising the test specificity. It will provide healthcare units and enterprises with solutions through scientifically proven algorithms, thus, saving a considerable amount of time and finances. This will eventually help in containing the spread of the pandemic in densely populated areas including vulnerably confined groups, such as nursing homes, hospitals, cruise ships, and military ships.Kamal Pasha MustafaGardazi Syed Fasih AliImtiaz FarihaQureshi Asma TalibAfrasiab RabiaDe Gruyterarticlecovid-19covid-19 testingbinary search methodsmachine learningartificial intelligenceScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 836-854 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 covid-19 testing binary search methods machine learning artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	covid-19 covid-19 testing binary search methods machine learning artificial intelligence Science Q Electronic computers. Computer science QA75.5-76.95 Kamal Pasha Mustafa Gardazi Syed Fasih Ali Imtiaz Fariha Qureshi Asma Talib Afrasiab Rabia Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation
description	Soon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testing kits for COVID-19 were manufactured for efficiently detecting COVID-19. However, due to limited resources in the densely populated countries, testing capacity even after a year is still a limiting factor for COVID-19 diagnosis on a larger scale and contributes to a lag in disease tracking and containment. Due to this reason, we started this study to provide a better cost-effective solution for enhancing the testing capacity so that the maximum number of people could get tested for COVID-19. For this purpose, we utilized the approach of artificial neural networks (ANN) to acquire the relevant data on COVID-19 and its testing. The data were analyzed by using Machine Learning, and probabilistic algorithms were applied to obtain a statistically proven solution for COVID-19 testing. The results obtained through ANN indicated that sample pooling is not only an effective way but also regarded as a “Gold standard” for testing samples when the prevalence of the disease is low in the population and the chances of getting a positive result are less. We further demonstrated through algorithms that pooling samples from 16 individuals is better than pooling samples of 8 individuals when there is a high likelihood of getting negative test results. These findings provide ground to the fact that if sample pooling will be employed on a larger scale, testing capacity will be considerably increased within limited available resources without compromising the test specificity. It will provide healthcare units and enterprises with solutions through scientifically proven algorithms, thus, saving a considerable amount of time and finances. This will eventually help in containing the spread of the pandemic in densely populated areas including vulnerably confined groups, such as nursing homes, hospitals, cruise ships, and military ships.
format	article
author	Kamal Pasha Mustafa Gardazi Syed Fasih Ali Imtiaz Fariha Qureshi Asma Talib Afrasiab Rabia
author_facet	Kamal Pasha Mustafa Gardazi Syed Fasih Ali Imtiaz Fariha Qureshi Asma Talib Afrasiab Rabia
author_sort	Kamal Pasha Mustafa
title	Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation
title_short	Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation
title_full	Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation
title_fullStr	Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation
title_full_unstemmed	Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation
title_sort	identification of efficient covid-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/0640f6590ead4984aee50070e5fbd922
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Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation

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