Machine Learning Application in Health
| 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.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. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12898 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | Coronavirus | |
| dc.subject | COVID-19 | |
| dc.subject | Machine learning | |
| dc.title | Machine Learning Application in Health |
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