论文标题
一种无监督的机器学习方法，用于评估纽约市Covid-19的邮政编码水平影响
An Unsupervised Machine Learning Approach to Assess the ZIP Code Level Impact of COVID-19 in NYC
论文作者
论文摘要
纽约市被公认为是新颖的冠状病毒大流行的世界震中。为了确定与纽约市Covid-19新案例增加速率高度相关的关键固有因素，我们提出了一个无监督的机器学习框架。基于以下假设：具有相似人口，社会经济和移动性模式的邮政编码区域可能会经历类似的爆发，我们选择了最相关的功能来执行可以最好地反映传播的聚类，并将其映射到9个可解释的类别。我们认为，我们的发现可以指导决策者通过采取正确的措施来及时预期并防止病毒的传播。
New York City has been recognized as the world's epicenter of the novel Coronavirus pandemic. To identify the key inherent factors that are highly correlated to the Increase Rate of COVID-19 new cases in NYC, we propose an unsupervised machine learning framework. Based on the assumption that ZIP code areas with similar demographic, socioeconomic, and mobility patterns are likely to experience similar outbreaks, we select the most relevant features to perform a clustering that can best reflect the spread, and map them down to 9 interpretable categories. We believe that our findings can guide policy makers to promptly anticipate and prevent the spread of the virus by taking the right measures.
