论文标题
纽约市的时间序列分析和地铁旋转门用法和共同的相关性
Time Series Analysis and Correlation of Subway Turnstile Usage and COVID-19 Prevalence in New York City
论文作者
论文摘要
在本文中，我们展示了纽约市大都会运输管理局提供的纽约市地铁的旋转栅门使用数据与纽约市卫生部报告的Covid-19死亡人数与COVID-19的死亡人数与案件之间的相关性。旋转栅门的使用数据不仅表明了该市地铁的用法，还表明人们的活动促进了从2020年3月至2020年5月的Covid-19 City Dellers经历的大量流行。尽管这种相关性很明显，但以前没有提供任何证据。在这里，我们通过应用长期短期记忆神经网络来证明这种相关性。我们表明，Covid-19的患病率与死亡的相关性考虑了报告死亡的孵化和有症状阶段。建立了这种相关性后，我们估计了当使用自动回归综合运动平均模型减少死亡人数后，199例死亡人数和病例的数量将接近零时，我们估计了日期。我们还通过对数据集进行回顾并将其与报告的日期进行比较时，估计了第一个病例和死亡发生的日期。
In this paper, we show a strong correlation between turnstile usage data of the New York City subway provided by the Metropolitan Transport Authority of New York City and COVID-19 deaths and cases reported by the New York City Department of Health. The turnstile usage data not only indicate the usage of the city's subway but also people's activity that promoted the large prevalence of COVID-19 city dwellers experienced from March to May of 2020. While this correlation is apparent, no proof has been provided before. Here we demonstrate this correlation through the application of a long short-term memory neural network. We show that the correlation of COVID-19 prevalence and deaths considers the incubation and symptomatic phases on reported deaths. Having established this correlation, we estimate the dates when the number of COVID-19 deaths and cases would approach zero after the reported number of deaths were decreasing by using the Auto-Regressive Integrated Moving Average model. We also estimate the dates when the first cases and deaths occurred by back-tracing the data sets and compare them to the reported dates.
