Abstract—Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.
Index Terms—Coronavirus infection, web responsive application, COVID-19 statistics, prediction model, neural networks.
I. Clitan, A. Puscasiu, V. Muresan, and M. Abrudean are with the Automation Department, Technical University of Cluj-Napoca, Romania (e-mail: iulia.clitan@aut.utcluj.ro, adela.puscasiu@aut.utcluj.ro, vlad.muresan@aut.utcluj.ro, mihai.abrudean@aut.utcluj.ro).
M. L. Unguresan is with the Physics and Chemistry Department, Technical University of Cluj-Napoca, Romania (e-mail: mihaela.unguresan@chem.utcluj.ro).
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Cite: Iulia Clitan, Adela Puscasiu, Vlad Muresan, Mihaela Ligia Unguresan, and Mihail Abrudean, "Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)," International Journal of Modeling and Optimization vol. 11, no. 3, pp. 70-74, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
(CC BY 4.0).
