Huang Ganyu, Pan Qiaoyi, Zhao Shuangying, Gao Yucen, Gao Xiaofeng
1SJTU-ParisTech Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai, 200240 China.
2School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
J Shanghai Jiaotong Univ Sci. 2020;25(2):140-146. doi: 10.1007/s12204-020-2167-2. Epub 2020 Apr 7.
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.
2019年12月12日，一种名为COVID-19的新型冠状病毒疾病开始从中国武汉蔓延至全球。考虑此次疫情的未来趋势既有用又迫切。我们建立了4+1五组模型来预测COVID-19疫情的发展。在该模型中，我们使用收集到的数据来校准参数，并让康复率和死亡率根据实际情况变化。此外，我们提出了BAT模型，它由三部分组成：返程高峰模拟（Back）、层次分析法（AHP）以及逼近理想解排序法（TOPSIS），以确定大学生的最佳返程日期。我们还讨论了未来可能出现的一些因素的影响，如二次感染、有效药物的出现以及从韩国到中国的人口流动。
