Author: Raj Dandekar; George Barbastathis
Title: Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning Document date: 2020_4_6
ID: 222c1jzv_8
Snippet: The copyright holder for this preprint . https://doi.org/10.1101/2020.04.03.20052084 doi: medRxiv preprint We trained the models using data starting from the dates when the 500 th infection was recorded in each region: 24 th January, 27 th February, and 22 nd February for Wuhan, Italy and South Korea, respectively; and up to about a month thereafter. The respective models, superimposed over the actual recorded data, are in Figures 2a, 4a and 6a, .....
Document: The copyright holder for this preprint . https://doi.org/10.1101/2020.04.03.20052084 doi: medRxiv preprint We trained the models using data starting from the dates when the 500 th infection was recorded in each region: 24 th January, 27 th February, and 22 nd February for Wuhan, Italy and South Korea, respectively; and up to about a month thereafter. The respective models, superimposed over the actual recorded data, are in Figures 2a, 4a and 6a, generally showing good agreement. The respective forecasts are in Figures 3, 5 and 7. As can be seen, the model with quarantine control included is able to capture the plateau in the infected case count, as opposed to the standard SIR and SEIR models shown in Figure . CC-BY-NC-ND 4.0 International license It is made available under a preprint in perpetuity.
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