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

Raj Dandekar; George Barbastathis

An important assumption of the SEIR and SIR models is homogeneous mixing among the subpopulations. Therefore, they cannot account for social distancing or mass quarantine effects. Additional assumptions are uniform susceptibility and disease progress for every individual; and that no spreading occurs through animals or other non-human means. Alternatively, the models may be interpreted as quantifying the statistical expectations on the respective mean populations, while deviations from the model's assumptions contribute to statistical fluctuations around the means. We applied both SEIR and SIR models to the case of Wuhan only. The results are shown in Figure 1 and verify that these models fail to predict the early arrest of infectious spread due to quarantine policies. Initial conditions The starting point t = 0 was the day at which 500 infected cases were detected, i.e., I(t = 0) = 500. The initial number of susceptible individuals was S(t = 0) = 11 million, Wuhan's population. The initial exposed population was assumed to be E(t = 0) = 20 × I(t = 0) in accordance with (Read et al. 2020; Wang et al. 2020) and the number of recovered individuals was set to a very small value R(t = 0) ≈ 10. Parameter estimation The time resolved data for the infected, I data and recovered, . CC-BY-NC-ND 4.0 International license It is made available under a preprint in perpetuity.