A Two-Phase Dynamic Contagion Model for COVID-19

TL;DR

This paper introduces a two-phase stochastic contagion model for COVID-19 that captures variable infectivity and assesses intervention effects, providing insights into epidemic size, duration, and intervention timing based on real data.

Contribution

The paper presents a novel two-phase dynamic contagion process model that incorporates randomness in infectivity and estimates key epidemiological metrics from real COVID-19 data.

Findings

Model aligns with COVID-19 incubation times

Estimates of epidemic size and duration are consistent with observed data

Time lag of intervention effects varies across regions

Abstract

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.