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dc.contributor.authorBhattasali, Tapalina-
dc.date.accessioned2023-04-28T20:08:11Z-
dc.date.available2023-04-28T20:08:11Z-
dc.date.issued2020-06-
dc.identifier.issn2616-6127-
dc.identifier.issn2617-4383-
dc.identifier.otherhttps://doi.org/10.32010/26166127.2020.3.1.32.45-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/43-
dc.description.abstractCOVID-19 pandemic has spread all over the world within a short period. It has changed every aspect of our daily lives significantly. The number of infected cases and the number of deaths are increasing day by day in many countries. Consequently, the situation becomes out of control. Due to recent advances in computational technologies, this paper focuses on the analytics part to assess various risks associated with the COVID-19 outbreak, which can be used to combat the severe effects of pandemics. Pandemic analytics is used to understand the spread pattern of pandemics by using the concept of artificial intelligence, machine learning, blockchain, and big data analytics. It is also required to evaluate policy for disease control. Based on the nature of the pandemic, a theoretical mathematical model is designed to predict the risks associated with the population all over the world. The analysis part is capable of forecasting the status and of answering various questions that arise from various parts of the world, such as the dependency of COVID-19 infection with sex, age, location, temperature, etc. Pandemic analytics is also used to visualize the official data before making any significant decisions.en_US
dc.language.isoenen_US
dc.publisherAzerbaijan Journal of High Performance Computingen_US
dc.subjectPandemicen_US
dc.subjectAnalyticsen_US
dc.subjectOutbreaken_US
dc.subjectCoronavirusen_US
dc.subjectCOVID-19en_US
dc.titlePANDEMIC ANALYTICS TO ASSESS RISK OF COVID-19 OUTBREAKen_US
dc.typeArticleen_US
dc.source.journaltitleAzerbaijan Journal of High Performance Computingen_US
dc.source.volume3en_US
dc.source.issue1en_US
dc.source.beginpage32en_US
dc.source.endpage45en_US
dc.source.numberofpages14en_US
Appears in Collections:Azerbaijan Journal of High Performance Computing

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