A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China

Alberto Aleta; Qitong Hu; Jiachen Ye; Peng Ji; Yamir Moreno

We implement a stochastic SEIR-metapopulation model to simulate the spread of the epidemic across mainland China. The model can be divided in two discrete processes: the disease dynamics governed by a SEIR compartmental model; and the mobility of individuals. In a SEIR compartmental model individuals are assigned into each group according to their health status: susceptible (S) if they are susceptible to the disease; exposed (E) if they have been infected but are still asymptomatic; infected (I) once the incubation period is over and the individuals show symptoms and can infect others; and removed (R) when they are either recovered or deceased. Within each subpopulation (henceforth, region), the transition between compartments results from the following rules, iterated at each time step, corresponding to 1 day: S→E: Susceptible individuals in region i might get infected with probability P (S → I) = 1 − (1 − R 0 /(T E N i )) I i , where R 0 is the reproduction number, T E the mean incubation time, N i the number of individuals in region i and I i the number of infected individuals in mentioned region. E→I: Exposed individuals enter the infected compartment with a rate inversely proportional to the mean latent period, T E .