基于SEIR的新型冠状肺炎预测与住院优化

SEIR-Based Prediction of COVID-19 and Optimization of Hospitalization

摘要: 文章从网络上公布的数据中，收集疫情爆发之初每日更新的新增感染，并利用SEIR模型模拟疫情扩散情况，建立新型冠状肺炎的传播扩散的数学模型，预测并画出感染者、易感者、健康者等人群的数量随时间变化的趋势图，与实际疫情数据进行对比，探讨增强或减弱隔离强度对控制疫情的影响和影响新型冠状肺炎的传播范围的指标。然后，对于患者到达医院的时间服从泊松分布，治疗时间服从负指数分布的假设，根据爬取的数据，以平均病床周转次数为优化指标，参考了优化问题中的排队论思想，采用优先级随时间变化的规则来进行病床安排，所建立的安排方案具有明显的优越性，能有效地缩短病人排队队列长度。

Abstract: Articles from the published data on the network, collect updated daily at the beginning of the outbreak of new infections, and use the SEIR model to simulate the spread of disease, establish a mathematic model of the spread of COVID-19, predict and draw the infection, the number of susceptible people, healthy people change over time trend diagram, compared with the actual epidemic data, explore the impact of strengthening or weakening the quarantine intensity on the control of the epidemic and the indicators affecting the transmission range of COVID-19. Then, for the patient to hospital in time to obey the poisson distribution, treatment time obey negative exponential distribution assumption, according to crawl, at an average bed turns in order to optimize the index and reference for the optimization problem of queuing theory, use the priority rules change over time for bed arrangement, the arrangement scheme has obvious superiority. It can effectively shorten the length of the patient queue.