Title Real-time neural network based predictor for cov19 virus spread /
Authors Wieczorek, Michał ; Siłka, Jakub ; Połap, Dawid ; Woźniak, Marcin ; Damaševičius, Robertas
DOI 10.1371/journal.pone.0243189
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Is Part of PLoS One.. San Francisco : Public Library of Science. 2020, vol. 15, iss. 12, art. no. e0243189, p. 1-18.. ISSN 1932-6203. eISSN 1932-6203
Keywords [eng] neural network ; prediction model ; cov19 virus spread
Abstract [eng] Since the epidemic outbreak in early months of 2020 the spread of COVID-19 has grown rapidly in most countries and regions across the World. Because of that, SARS-CoV-2 was declared as a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, by The World Health Organization (WHO). That's why many scientists are working on new methods to reduce further growth of new cases and, by intelligent patients allocation, reduce number of patients per doctor, what can lead to more successful treatments. However to properly manage the COVID-19 spread there is a need for real-time prediction models which can reliably support various decisions both at national and international level. The problem in developing such system is the lack of general knowledge how the virus spreads and what would be the number of cases each day. Therefore prediction model must be able to conclude the situation from past data in the way that results will show a future trend and will possibly closely relate to the real numbers. In our opinion Artificial Intelligence gives a possibility to do it. In this article we present a model which can work as a part of an online system as a real-time predictor to help in estimation of COVID-19 spread. This prediction model is developed using Artificial Neural Networks (ANN) to estimate the future situation by the use of geo-location and numerical data from past 2 weeks. The results of our model are confirmed by comparing them with real data and, during our research the model was correctly predicting the trend and very closely matching the numbers of new cases in each day.
Published San Francisco : Public Library of Science
Type Journal article
Language English
Publication date 2020
CC license CC license description