KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
Department of Statistics, University of Haifa, Haifa, Israel
Various studies have provided a wide variety of mathematical and statistical models for early epidemic prediction of the COVID-19 outbreaks in Mainland China and other epicentres worldwide. In this paper, we present an integrated modelling framework, which incorporates typical exponential growth models, dynamic systems of compartmental models and statistical approaches, to depict the trends of COVID-19 spreading in 33 most heavily suffering countries. The dynamic system of SIR-X plays the main role for estimation and prediction of the epidemic trajectories showing the effectiveness of containment measures, while the other modelling approaches help determine the infectious period and the basic reproduction number. The modelling framework has reproduced the subexponential scaling law in the growth of confirmed cases and adequate fitting of empirical time-series data has facilitated the efficient forecast of the peak in the case counts of asymptomatic or unidentified infected individuals, the plateau that indicates the saturation at the end of the epidemic growth, as well as the number of daily positive cases for an extended period.
To cite this article: Jiawei Xu & Yincai Tang (2021) An integrated epidemic modelling framework for the real-time forecast of COVID-19 outbreaks in current epicentres, Statistical Theory and Related Fields, 5:3, 200-220, DOI: 10.1080/24754269.2021.1872131
To link to this article: https://doi.org/10.1080/24754269.2021.1872131