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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 citys subway but also peoples 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.
This paper continues to highlight trends in mobility and sociability in New York City (NYC), and supplements them with similar data from Seattle, WA, two of the cities most affected by COVID-19 in the U.S. Seattle may be further along in its recovery
New York has become one of the worst-affected COVID-19 hotspots and a pandemic epicenter due to the ongoing crisis. This paper identifies the impact of the pandemic and the effectiveness of government policies on human mobility by analyzing multiple
Redlining is the discriminatory practice whereby institutions avoided investment in certain neighborhoods due to their demographics. Here we explore the lasting impacts of redlining on the spread of COVID-19 in New York City (NYC). Using data availab
The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economi
Group testing allows saving chemical reagents, analysis time, and costs, by testing pools of samples instead of individual samples. We introduce a class of group testing protocols with small dilution, suited to operate even at high prevalence ($5%-10