Abstract
On 12 December 2019, a novel coronavirus disease, named COVID-19, began to spread around the world from Wuhan, China. It is useful and urgent to consider the future trend of this outbreak. We establish the 4+1 penta-group model to predict the development of the COVID-19 outbreak. In this model, we use the collected data to calibrate the parameters, and let the recovery rate and mortality change according to the actual situation. Furthermore, we propose the BAT model, which is composed of three parts: simulation of the return rush (Back), analytic hierarchy process (AHP) method, and technique for order preference by similarity to an ideal solution (TOPSIS) method, to figure out the best return date for university students. We also discuss the impacts of some factors that may occur in the future, such as secondary infection, emergence of effective drugs, and population flow from Korea to China.
Key words: epidemic dynamics model, nonlinear least squares; analytic hierarchy process (AHP); technique for order preference by similarity to an ideal solution (TOPSIS)
Nomenclature
- c
The average number of contacts of an exposed person without isolation each day
- n
The number of individuals
- nD
The death toll
- nE
The number of exposed individuals
- nI
The number of infectious individuals
- nR
The number of recovered individuals
- nS
The number of susceptible individuals
- N
The total population of China
- p
Intensity of isolation for exposed individuals
- r
Correlation coefficient
- R2
Coefficient of determination
- t0
Moment when the government began to take measures
- t
Outbreak duration
- α
Incubation rate
- β
Infectious rate of contacts of an exposed person
- γ
Recovery rate
- μ
Pneumonia mortality
Footnotes
Foundation item: the National Key Research and Development Program of China (No. 2018YFB1004700), the National Natural Science Foundation of China (Nos. 61872238 and 61972254), the Shanghai Science and Technology Fund (No. 17510740200), and the CCFHuawei Database System Innovation Research Plan (No. CCF-Huawei DBIR2019002A)
These authors contributed equally to this work.
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