The Pandemic Holiday Blip in New York City

Abstract

When it comes to pandemics, such as the one caused by the Coronavirus disease COVID-19, various issues and problems have arisen for the healthcare infrastructure and institutions. With increasing number of patients in need of urgent medical care and hospitalizations, the healthcare systems and regional hospitals may approach their maximum service capacity and may face shortage of various parameters, such as supplies including PPE, medications, therapeutic devices, ventilators, beds, and many more. The article at hand describes the development and framework of a simulation model that enables the modeling and evaluation of the COVID-19 pandemic progress. To achieve this, the model dynamically mimics and simulates the developments and time-dependent behavior of various crucial parameters of the pandemic, among others, the daily infection numbers and death rate. In addition, the model enables the simulation of single events and scenarios that occur outside of the regular pandemic developments as anomalies, such as holidays. Unlike traditional models, the proposed framework is based on factors and parameters closely derived from reality, such as the contact rate of individuals, which allows for a much more realistic representation. In addition, the real connection enables the assessment of effects of various influences regarding the development and progress of the pandemic, such as hospitalization numbers over time. All the aforementioned points are possible within the simulation framework and do not require awaiting the unfolding of the effects in reality. Thus, the model is capable of dynamically predicting how different scenarios turn out. The abilities of the model are demonstrated, illustrated, and proven in a specific case study that shows the impact of holidays, such as Passover and Easter in New York City when quarantine measures might have been ignored, and an increase in extended family gatherings temporarily occurred. As a result, the simulation showed significant impacts and disproportionate number of patients in need of medical care that could be potentially detrimental in reality. For example, compared to the previous trajectory of the pandemic, for a temporary increase of 50% in the contact rate of individuals, the model showed that the total number of cases would increase by 461 090, the maximum number of required hospitalizations would rise to 79 733, and the total number of fatalities would climb by 19 125 over 90 days. In addition to its function and proven capabilities, the model can and is furthermore planned to be adapted to other areas, not necessarily only metropolitan regions in order to expand the utilization of its predictive power. Such predictions could be used to derive regulatory measures and to test various policies for COVID-19 containment.

Artificial Intelligence and Information Processing: A Systematic Literature Review

“…Machine learning enables computers to automatically learn and improve from experience and is an important means of realizing AI [2,3]. Mathematical problems play an indispensable role in machine learning algorithms, such as linear regression [4,5], support vector machines, decision trees [6,7], random forests, deep learning [8][9][10][11], and scheduling [12], which involve various mathematical concepts, including optimization [13][14][15], matrix decomposition, probability theory [16,17], weight considerations [18][19][20], simulations [21][22][23], heuristics algorithms [24,25], and statistics [26][27][28].…”