【摘要】：在流行病人群分布模型的研究中,人群流行病學在空間上呈現(xiàn)非周期、幾何指數(shù)、非線性的特征,分布過程復雜度較高,很難運用單一模型控制。傳統(tǒng)的模型僅以小周期模型為基礎(chǔ),對大規(guī)模疾病分布建模的過程,以多模型組合逼近完成,缺少約束過程,造成復雜度很高,建模效果不好。為了避免上述缺陷,提出基于約束分類優(yōu)化算法的人群流行病學空間分布模型。根據(jù)約束分類優(yōu)化相關(guān)原理,對流行病的空間分布進行分層約束,得到較為合理的人群流行病種群的分布情況,對流行病種群中的所有個體進行編號處理,通過迭代計算得到最優(yōu)分布區(qū)域的計算結(jié)果個體。通過臨近地區(qū)流行病分布比例,得到人群流行病學的空間位置分布情況。實驗結(jié)果表明,利用改進算法進行人群流行病學空間分布,能夠極大的提高分布建模的準確性,降低建模復雜度。
[Abstract]:In the study of epidemic population distribution model, population epidemiology presents aperiodic, geometric exponent, nonlinear characteristics in space, the complexity of distribution process is high, it is difficult to use a single model to control. The traditional model is only based on the small-period model, and the modeling process of large-scale disease distribution is completed by multi-model combination approach, which is lack of constraint process, resulting in high complexity and poor modeling effect. In order to avoid these defects, a population epidemiology spatial distribution model based on constrained classification optimization algorithm is proposed. According to the principle of constraint classification optimization, the spatial distribution of epidemic is stratified, and the distribution of population epidemic population is obtained, and all individuals in epidemic population are numbered. The individual results of the optimal distribution region are obtained by iterative calculation. The spatial distribution of population epidemiology was obtained by epidemic distribution in adjacent areas. The experimental results show that the improved algorithm can greatly improve the accuracy of distribution modeling and reduce the complexity of modeling.
【作者單位】： 廣西醫(yī)科大學信息管理與信息系統(tǒng)(醫(yī)學)系;
【基金】：信息中心實驗動物信息平臺電子物理網(wǎng)絡(luò)支撐體系建設(shè)應用研究(2060503科技條件專項) 2012年自治區(qū)科技基礎(chǔ)條件平臺建設(shè)財政補助項目
【分類號】：R181;TP391.9

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