Author: Laszlo Robert Kolozsvari; Tamas Berczes; Andras Hajdu; Rudolf Gesztelyi; Attila TIba; Imre Varga; Gergo Jozsef Szollosi; Szilvia Harsanyi; Szabolcs Garboczy; Judit Zsuga
Title: Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence Document date: 2020_4_22
ID: 35xpmdbj_9
Snippet: The aim of the current study was to use the available official data as a training dataset, followed 143 by predicting the possible outcomes of the COVID-19 pandemic using AI-based RNNs, then 144 compare the predictions with the observed data. in China were from Hubei province, only data from that province was included. For each 154 country, the date of the first infection was set as day 1 for the disease time scale. (Fig 1) 155 All rights reserve.....
Document: The aim of the current study was to use the available official data as a training dataset, followed 143 by predicting the possible outcomes of the COVID-19 pandemic using AI-based RNNs, then 144 compare the predictions with the observed data. in China were from Hubei province, only data from that province was included. For each 154 country, the date of the first infection was set as day 1 for the disease time scale. (Fig 1) 155 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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