Abstract
This study aims to establish a model-based framework for inferring key transmission characteristics of the newly emerging outbreak of the coronavirus disease 2019 (COVID-19), especially the epidemic dynamics under quarantine conditions. Inspired by the shifting therapeutic levels and capacity at different stages of the COVID-19 pandemic, we propose a modified SEIR model with a two-phase removal rate of quarantined hosts undergoing continuously tunable transition. We employ the Markov Chain Monte Carlo (MCMC) approach for inferring and forecasting the epidemiological dynamics from the publicly available surveillance reports. The effectiveness of a short-term prediction is illustrated by adopting the data sets from 10 demographic regions including Chinese mainland and South Korea. In the surveillance period, the average R0 ranges from 1.74 to 3.28, and the median of the mean latent period does not exceed 10 days across the surveillance regions.
Highlights
An outbreak of a novel coronavirus disease that causes severe acute respiratory syndrome (SARS-CoV) emerged in December 2019, and was subsequently declared a public health emergency of international concern by the World Health Organization (WHO) on January 30, 2020
Walker et al [12] used an age-structured stochastic SEIR model to fit the key parameters of the observed transmission dynamics of COVID-19
A series of papers [9, 10, 14] developed the quarantined SEIR-type models by imposing restrictions on mobility of patients, with model parameters fitted to the data reported by China CDC using the Markov Chain Monte Carlo (MCMC) method
Summary
An outbreak of a novel coronavirus disease that causes severe acute respiratory syndrome (SARS-CoV) emerged in December 2019, and was subsequently declared a public health emergency of international concern by the World Health Organization (WHO) on January 30, 2020. Under the increasingly severe situations of epidemic prevention and control in many countries over the world, the epidemiological modeling using surveillance data provides useful mathematical tools for identifying and predicting the disease outbreaks. A series of papers [9, 10, 14] developed the quarantined SEIR-type models by imposing restrictions on mobility of patients, with model parameters fitted to the data reported by China CDC using the Markov Chain Monte Carlo (MCMC) method. Li et al [3] introduced a networked SEIR-type model with migration data within China, and employed Bayesian inference to infer critical epidemiological characteristics
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