Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries

Edward De Brouwer; Daniele Raimondi; Yves Moreau

We performed a parametric Bayesian regression (see Methods) on the mainland China COVID-19 epidemic data by training a SEIR model on the cumulative cases time series, with the goal of inferring the change in R 0 = β/γ produced by the increasingly stringent containment measures introduced by the Chinese government. Such lockdowns and quarantines mainly aim at reducing the frequency of the contacts β −1 between individ- uals. We thus used the β i before and after the introduction of each containment measures and the γ as trainable parameters. In this study we used an average incubation time δ −1 = 5.2 days, as reported in (12) . The implementation of the first containment measure in China happened on February 23, 2020, when all public transportation was suspended in Wuhan, corresponds to a 66% decrease in the inferred R 0 , bringing it down from 3.36 (CI 95% [2.88,4 .29]) to 1.15 (CI 95% [0.92,1.4]). The introduction of the more stringent measures on February 23, 2020, including closing all non-essential companies and manufacturing plants in Hubei province corresponded to a further reduction to the R 0 identified by our SEIR model, down to 0.19.