A two-phase dynamic contagion model for COVID-19

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

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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; coronavirus; stochastic intensity model; two-phase dynamic contagion process; lockdown; Stochastic epidemic model (search for similar items in EconPapers)
JEL-codes: E60 H00 I10 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2021-07-01
New Economics Papers: this item is included in nep-hea and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Results in Physics, 1, July, 2021, 26. ISSN: 2211-3797

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