  In this paper, we propose a continuous-time stochastic intensity model,
namely, two-phase dynamic contagion process(2P-DCP), for modelling the epidemic
contagion of COVID-19 and investigating the lockdown effect based on the
dynamic contagion model introduced by Dassios and Zhao (2011). It allows
randomness to the infectivity of individuals rather than a constant
reproduction number as assumed by standard models. Key epidemiological
quantities, such as the distribution of final epidemic size and expected
epidemic duration, are derived and estimated based on real data for various
regions and countries. The associated time lag of the effect of intervention in
each country or region is estimated. Our results are consistent with the
incubation time of COVID-19 found by recent medical study. We demonstrate that
our model could potentially be a valuable tool in the modeling of COVID-19.
More importantly, the proposed model of 2P-DCP could also be used as an
important tool in epidemiological modelling as this type of contagion models
with very simple structures is adequate to describe the evolution of regional
epidemic and worldwide pandemic.
