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_1
Snippet: WHO with R0 suggested to range between 1.4 and 2.5. More recent analyzes have indicated 109 higher R0 values around 3 (with the mean and median R0 for published estimates being 3.28 110 and 2.79, respectively). 11,15 111 The daily number of the newly diagnosed infections -epidemic curves 112 The initial epidemic curves of the COVID-19 outbreak from Hubei, China showed a mixed 113 pattern, indicating that early cases were likely from a continuous .....
Document: WHO with R0 suggested to range between 1.4 and 2.5. More recent analyzes have indicated 109 higher R0 values around 3 (with the mean and median R0 for published estimates being 3.28 110 and 2.79, respectively). 11,15 111 The daily number of the newly diagnosed infections -epidemic curves 112 The initial epidemic curves of the COVID-19 outbreak from Hubei, China showed a mixed 113 pattern, indicating that early cases were likely from a continuous common source e.g. from 114 several zoonotic events in Wuhan, followed by secondary and tertiary transmission providing There are different mathematical models that may demonstrate and predict the dynamics of the 134 different infectious diseases. 18 These models, used to simulate the dynamics of infectious 135 diseases, may be based on statistical, mathematical, empirical or machine learning methods. 19
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