Fathi-Kazerooni Sina, Rojas-Cessa Roberto, Dong Ziqian, Umpaichitra Vatcharapan
Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
Department of Electrical and Computer Engineering, College of Engineering and Computing Sciences, New York Institute of Technology, New York, NY, 10023, USA.
Infect Dis Model. 2021;6:183-194. doi: 10.1016/j.idm.2020.11.006. Epub 2020 Dec 4.
In this paper, we show a strong correlation between turnstile entries data of the New York City (NYC) subway provided by NYC Metropolitan Transport Authority and COVID-19 deaths and cases reported by the NYC Department of Health from March to May 2020. This correlation is obtained through linear regression and confirmed by the prediction of the number of deaths by a Long Short-Term Memory neural network. The correlation is more significant after considering incubation and symptomatic phases of this disease as experienced by people who died from it. We extend the analysis to each individual NYC borough. We also estimate the dates when the number of COVID-19 deaths and cases would approach zero by using the Auto-Regressive Integrated Moving Average model on the reported deaths and cases. We also backward forecast the dates when the first cases and deaths might have occurred.
在本文中，我们展示了纽约市大都会交通管理局提供的纽约市（NYC）地铁旋转栅门进入数据与纽约市卫生部在2020年3月至5月报告的COVID-19死亡人数和病例数之间的强相关性。这种相关性是通过线性回归获得的，并由长短期记忆神经网络对死亡人数的预测得到证实。在考虑了死于该疾病的人所经历的潜伏期和症状期后，这种相关性更为显著。我们将分析扩展到纽约市的每个行政区。我们还使用自回归积分移动平均模型对报告的死亡人数和病例数进行估计，以确定COVID-19死亡人数和病例数接近零的日期。我们还向后预测了首例病例和死亡可能发生的日期。
