Epidemic analysis of COVID-19 in China by dynamical modeling

Liangrong Peng; Wuyue Yang; Dongyan Zhang; Changjing Zhuge; Liu Hong

A. Generalized SEIR model {S(t), P (t), E(t), I(t), Q(t), R(t), D(t)} denoting at time t the respective number of the susceptible cases, insusceptible cases, exposed cases (infected but not yet be infectious, in a latent period), infectious cases (with infectious capacity and not yet be quarantined), quarantined cases (confirmed and infected), recovered cases and closed cases (or death). The adding of a new quarantined sate is driven by data, which together with the recovery state takes replace of the original R state in the classical SEIR model. Their relations are given in Fig. 1 and characterized by a group of ordinary differential equations (or difference equations if we consider discrete time, see SI). Constant N = S + P + E + I + Q + R + D is the total population in a certain region. The coefficients {α, β, γ −1 , δ −1 , λ(t), κ(t)} represent the protection rate, infection rate, average latent time, average quarantine time, cure rate, and mortality rate, separately. Especially, to take the improvement of public health into account, such as promoting wearing face masks, more effective contact tracing and more strict locking-down 4 All rights reserved. No reuse allowed without permission.