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从而得到2011-2020年深圳市和各区的床位需求状况（具体数据见表12与表15） 更多：https://www.bmcx.com/ 。 关键字：多元回归修正弗里希法EVIEWS莱斯利模型马尔柯夫预测

本文针对深圳市人口与医疗需求预测问题，以人口的变化情况与结构特征为问题的切入点，综合分析了2000 2010年深圳市及各区人口及其结构、2000年以来深圳市各类医院床位需求状况以及某些疾病的就医情况的数据，建立了人口增长模型和Leslie模型预测模型，运用Excel、Eviews等软件得出了深圳市未来十年人口与医疗需求的预测结果。 针对问题一，在对近十年深圳市常住人口与非常住人口以及预测出未来十年人口数量、特征的基础上，预测出深圳市和各区的床位需求状况。首先利用Excel建立人口增长模型，预测出深圳市未来十年人口的变化趋势；以5岁为一个间隔，将人口分为21组，建立Leslie模型，预测出各年龄组人口占深圳市总人口的比重；为减了降低对未来男女性别比预测误差，采用了两种方法进行预测，运用Excel预测出深圳市未来十年性别比。其次，在之前的分析基础上 更多：https://www.bmcx.com/ ，运用多元回归分析方法构建了床位需求影响度模型，用Eviews求解出预测模型，从而得到2011 2020年深圳市和各区的床位需求状况（具体数据见表12与表15）。 针对问题二，选择常见的3种疾病，即小儿肺炎、分娩以及心肌梗塞进行分析，并将医院简单划分为3种类型，即综合医院、专科医院以及妇幼保健医院，据此分别预测出3种疾病的患者在不同类型医院的对床位的需求状况。首先分析有关的数据，通过线性拟合确定患这3种疾病各自的发病率，然后再根据问题一预测出的深圳市的年龄结构情况以及人口性别比，从而对未来十年各疾病的发病总人数进行人口预测。其次，利用病人数与床位数之间的关系，通过建立马尔科夫模型，分别预测出3种常见疾病患者对三种不同的医疗机构相应的床位需求量（具体数据见表）。 关键字：多元回归修正Frisch法 Eviews Leslie模型马尔柯夫预测

This article in view of the shenzhen population and medical treatment demand forecasting problems, with the changes of population and the structure characteristics of the breakthrough point for problems were analyzed, 2000-2010, shenzhen and district and its structure, population since 2000 hospital beds demand status of all kinds of shenzhen and some diseases of medical data,Establish the population growth model and Leslie model prediction model, by use of Excel software, Eviews concluded that the shenzhen over the next ten years population and medical needs the prediction result. in a, in recent ten years in shenzhen permanent population and very live population and predict future ten years of population,Based on the characteristics, predict shenzhen and the bed demand conditions. First use of Excel establishing a population growth model, and the prediction that the shenzhen future ten years population change trend; With five years old for a interval, the population is divided into 21 group, Leslie model establishment,Every age group predicts a population accounts for the proportion of the total population of shenzhen; To reduce the future of men and women reduce sex ratio at predicting error, the two methods to carry on the forecast, by use of Excel predict shenzhen future ten years sex ratio. Secondly 更多：https://www.bmcx.com/ , in the previous analysisUsing multiple regression analysis method for building beds sinokorea demand model, with Eviews to solve the prediction model, and get 2011-2020 shenzhen and the bed demand conditions (specific data table 12 and table 15). in two, choose three common diseases, which infantile pneumonia,Childbirth and myocardial infarction are analyzed, and will be divided into three kinds of hospital simple types, namely general hospital, specialized subject hospital maternity and child care and hospital, according to predict three respectively disease in different types of hospital beds to demand situation. First analysis the data,Through the linear fitting with the three kinds of disease to determine the incidence of their respective, then according to the question of a prediction that the age structure of shenzhen city and the sex, and the future ten years the total number of disease on population prediction. Secondly, the use of the relationship between the third and ChuangWeiShu,Through the establishment of markov model, separately predict three common disease patients out of three kinds of different medical establishment of corresponding demand for bed (specific data table). key word: multiple regression method Eviews Leslie model Frisch modified markov forecast

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