Inferring key epidemiological parameters and transmission dynamics of COVID-19 based on a modified SEIR model
| Autor: | Kazuyuki Aihara, Tianjiao Tang, Qian Guo, Lang Cao, Xiaoyan Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
2019-20 coronavirus outbreak
medicine.medical_specialty Coronavirus disease 2019 (COVID-19) Computer science Applied Mathematics Epidemic dynamics Markov chain Monte Carlo 010502 geochemistry & geophysics 01 natural sciences law.invention 03 medical and health sciences symbols.namesake 0302 clinical medicine Transmission (mechanics) law Modeling and Simulation Modelling and Simulation Pandemic Epidemiology Econometrics symbols Key (cryptography) medicine 030212 general & internal medicine 0105 earth and related environmental sciences |
| Zdroj: | Mathematical Modelling of Natural Phenomena |
| ISSN: | 1760-6101 0973-5348 |
| DOI: | 10.1051/mmnp/2020050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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