Data Analytics Research for COVID19 Pandemic
| dc.contributor.author | Tran, Thao | |
| dc.date.accessioned | 2022-01-18T21:20:07Z | |
| dc.date.available | 2022-01-18T21:20:07Z | |
| dc.date.issued | 2021-04-28 | |
| dc.description.abstract | The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. According to CDC, the first coronavirus case in the US has been identified in Washington state, and that was due to air travel from Wuhan, China. The most common way Covid-19 can spread is by human interaction, through respiratory droplets such as talking, coughing, sneezing, and more. We apply machine learning models to answer this problem. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12185 | |
| dc.language.iso | en_US | |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/3.0/us/ | |
| dc.subject | Machine learning | |
| dc.subject | Pandemic | |
| dc.subject | COVID-19 | |
| dc.title | Data Analytics Research for COVID19 Pandemic | |
| dc.type | Working Paper |