﻿ 埃博拉疫情控制模型

埃博拉疫情控制模型A Mathematical Model for Ebola Control

Abstract: This paper aims to analyze the cases of Ebola in West Africa, forecast the future trend of the spread of the virus, and provide some consulting opinions for related departments to make decisions. Firstly, we construct the SIR-L model for Ebola epidemics based on the classical infectious diseases SIR model and the Logistic model to simulate and predict the spread trends of the virus. We get that the Ebola has crossed the peak period in the three main countries, but the cases of the Ebola are still at a high level. Then we forecast the new cases in the next week: 88, 28 and 238 cases for Guinea, Liberia, and Sierra Leone, respectively. Secondly, by analyzing the data that we forecast, combing with the population size and geographical location of each country, a dynamical model is established based on gravity center method and gray theory, which are utilized to select the optimal distribution center. It reveals that River Cess, Bo, Mamou are the optimal distribution centers of Liberia, Sierra Leone, and Guinea, respectively. Sierra Leone, the hardest-hit area, is se-lected as the production country, and Bo is selected as the production center. Finally, based on the optimal distribution center and production center, we develop the distribution model of emergency supplies in three layers, which refers to some of advanced sophisticated logistics systems and operational experiences of modern enterprise logistics systems. That is to say, the production center is considered as the first layer of the network; the distribution center of the three countries is the second layer; and the town center of each country’s county is the third layer. We design a distribution network system based on the principle that the delivery time of the Ebola is the shortest in the beginning, and the cost of the rescue system is the lowest in the later period.

[1] WHO (2015) 2014 Ebola outbreak in West Africa—Case counts. http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html

[2] 姜启源, 谢金星, 叶俊, 等 (1993) 数学模型. 高等教育出版社, 北京.

[3] 叶星旸 (2006) 几类传染病动力学模型的研究. 硕士论文, 福建师范大学, 福州.

[4] 程颖, 刘军, 李昱, 等 (2014) 埃博拉病毒病: 病原学, 致病机制, 治疗与疫苗研究进展. 科学通报, 59, 2889- 2899.

[5] 杜少甫, 谢金贵, 刘作仪 (2013) 医疗运作管理: 新兴研究热点及其进展①. 管理科学学报, 3, 68-72.

[6] 彭小宁, 彭元 (1992) 两种计划免疫苗种需求预测模型. 怀化学院学报, 5, 006.

[7] 鲁晓春, 詹荷生 (2000) 关于配送中心重心法选址的研究. 北方交通大学学报, 6, 108-110.

[8] 陈森, 周峰 (2006) 基于灰色系统理论的物流需求预测模型. 统计与决策, 3, 59-60.

[9] 庞海云 (2012) 突发性灾害事件下应急物资分配决策优化过程研究. 硕士论文, 浙江大学, 杭州.

[10] 葛洪磊 (2012) 基于灾情信息特征的应急物资分配决策模型研究. 硕士论文, 浙江大学, 杭州.

[11] 桂维民 (2007) 应急决策简论. 中国应急管理, 12, 14-17.

[12] 孟超 (2007) 西蒙决策理论研究. 硕士论文, 西北大学, 西安.

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