Machine learning Models to Predict COVID-19 Cases

Ghadah Alshabana, Thao Tran, Marjan Saadati, Michael Thompson George, Ashritha Chitimalla
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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.
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预测COVID-19病例的机器学习模型
冠状病毒可通过与感染者的近距离接触通过空气传播。商用飞机可能是在乘客之间传播病毒和在地点之间传播病毒的一种方式。了解冠状病毒在哪里以及如何进入美国的重要性将有助于我们进一步了解这种疾病。航空旅客可能来自高感染率的国家或地区,很可能有接触病毒的风险。因此,当它们到达美国时,病毒很容易传播。在我们的分析中,我们利用机器学习来确定进入华盛顿特区都市区的航班数量是否对该市及周边地区报告的病例和死亡人数有影响。
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