A two-phase dynamic contagion model for COVID-19

Zezhun Chen; Angelos Dassios; Valerie Kuan; Jia Wei Lim; Yan Qu; Budhi Surya; Hongbiao Zhao

doi:10.1016/j.rinp.2021.104264

A two-phase dynamic contagion model for COVID-19

Results Phys. 2021 Jul:26:104264. doi: 10.1016/j.rinp.2021.104264. Epub 2021 May 13.

Authors

Zezhun Chen¹, Angelos Dassios¹, Valerie Kuan², Jia Wei Lim³, Yan Qu⁴, Budhi Surya⁵, Hongbiao Zhao⁶

Affiliations

¹ London School of Economics, United Kingdom.
² University College London, United Kingdom.
³ Brunel University London, United Kingdom.
⁴ Peking University, China.
⁵ Victoria University of Wellington, New Zealand.
⁶ Shanghai University of Finance and Economics, China.

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 [24]. 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.

Keywords: COVID-19; Lockdown; Primary: 60G55; Secondary: 60J75; Stochastic epidemic model; Stochastic intensity model; Two-phase dynamic contagion process.