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_10
Snippet: In the case of the US, similarly to the other regions, i.e. starting when the 500 th infection was recorded on March 8 th , we trained the model till the latest available data, i.e till 1 April 2020. The infected case count estimated by our model shows a good match with the actual data ( Figure 8a ). Forecasting results for Q(t), R t for a period of 1 month following the current US policy are in Figure 9 . In Figure 10a we forecast the number of .....
Document: In the case of the US, similarly to the other regions, i.e. starting when the 500 th infection was recorded on March 8 th , we trained the model till the latest available data, i.e till 1 April 2020. The infected case count estimated by our model shows a good match with the actual data ( Figure 8a ). Forecasting results for Q(t), R t for a period of 1 month following the current US policy are in Figure 9 . In Figure 10a we forecast the number of infections the US would experience starting from 1 st April if the US were to follow its current quarantine policy as opposed to gradually adjust to adopting the respective quarantine models learnt from the more reliable Wuhan, Italy and South Korea data. We arbitrarily set the adjustment period to 17 days; i.e till 17 April 2020. Figure 10b shows the effective quarantine rates Q(t) obtained from (4.17) that would apply to the US during the respective adjustment period. Details are in the Materials & Methods section.
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