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Machine Learning Application in Health

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dc.contributor.author Alshabana, Ghadah
dc.contributor.author Tran, Thao
dc.contributor.author Chitimalla, Ashritha
dc.contributor.author Thompson, Michael
dc.date.accessioned 2022-06-10T12:39:33Z
dc.date.available 2022-06-10T12:39:33Z
dc.date.issued 2022-06-10
dc.identifier.uri http://hdl.handle.net/1920/12898
dc.description.abstract Coronavirus can be transmitted through the air by close proximity to infected persons. Commercial aircraft are a likely way to both transmit the virus among passengers and move the virus between locations. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. On our analysis, we utilized machine learning to determine if the number of flights into the Washington DC Metro Area had an effect on the number of cases and deaths reported in the city and surrounding area. en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject Coronavirus en_US
dc.subject COVID-19 en_US
dc.subject Machine Learning en_US
dc.title Machine Learning Application in Health en_US


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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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