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In this paper, we show a strong correlation between turnstile usage data ofthe New York City subway provided by the Metropolitan Transport Authority ofNew York City and COVID-19 deaths and cases reported by the New York CityDepartment of Health. The turnstile usage data not only indicate the usage ofthe city's subway but also people's activity that promoted the large prevalenceof COVID-19 city dwellers experienced from March to May of 2020. While thiscorrelation is apparent, no proof has been provided before. Here we demonstratethis correlation through the application of a long short-term memory neuralnetwork. We show that the correlation of COVID-19 prevalence and deathsconsiders the incubation and symptomatic phases on reported deaths. Havingestablished this correlation, we estimate the dates when the number of COVID-19deaths and cases would approach zero after the reported number of deaths weredecreasing by using the Auto-Regressive Integrated Moving Average model. Wealso estimate the dates when the first cases and deaths occurred byback-tracing the data sets and compare them to the reported dates.

Time Series Analysis and Correlation of Subway Turnstile Usage and COVID-19 Prevalence in New York City