Quantifying the effect of quarantine control in Covid-19 infectious spread using machine learning

Raj Dandekar; George Barbastathis

All four regions that we applied our model to have developed infected and exposed populations that are sufficiently large to train our models. The first three are comparable in terms of population (11 million, 60 million and 52 million, respectively) and almost complete isolation from inbound travel while the USA has a much larger population (327 million), with increasing travel restrictions since mid March 2020. Leveraging the insights gained through reliable prediction and estimation in Wuhan, South Korea and Italy, we make forecasting predictions regarding the infection spread in the USA; thus making our model informative for quarantine and social distancing policy guidelines and regulations. is the author/funder, who has granted medRxiv a license to display the (which was not peer-reviewed) Figure 1 shows results from the classical SEIR and SIR models applied to Wuhan data. Neither model can recover the stagnation seen in the actual infected number, about 30 days post the detection of the 500 th infected case in Wuhan, i.e. 24 th January, 2020. The neural network model trained to include quarantine, on the other hand, does predict this stagnation; see below.