Analysis of Experimental Data Needed for Synthesis of the Mathematical Model of Predicting the Distribution of Allergen Spring of Municipal Alternaria

V. B. Mokin, V. Rodinkova, M. V. Dratovanyi
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Abstract

To predict the distribution of the allergenic fungal spores that have a negative effect on the health of a large number of people, it is necessary to synthesize predictive mathematical models that interconnect in time the concentration of allergenic fungal spores that spread as a result of the movement of air masses, which is the initial coordinate of the model, and the accompanying meteorological factors such as wind speed, air temperature, air humidity and a number of other factors that are input coordinates of the model. To determine and identificate the structure of the parameters of such predictive mathematical models, we need the results of an experimental study of the interactions between all these coordinates, which in their original form cannot be used directly to identify the above class of mathematical models, since they are nonstationary stochastic. In this paper, the following processing of the results of an experimental study of all these non-stationary stochastic coordinates characterizing the process of distribution of the allergenic fungal spores obtained on the basis of the Vinnitsa National Medical University, leads to such characteristics of these coordinates, which can then be used for synthesis of predictive mathematical models. These characteristics are stationary zonation of coordinates due to the transition from their measured values to their increments and correlation coefficients and autocorrelation and mutual correlation functions, calculated with regard to the increments of all the considered coordinates.
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城市交替菌变应原分布预测数学模型合成所需实验数据分析
为了预测对大量人群健康产生负面影响的致敏真菌孢子的分布,需要综合预测数学模型,将因气团运动而传播的致敏真菌孢子的浓度作为模型的初始坐标,与伴随的气象因子如风速、气温、空气湿度和其他一些因素是模型的输入坐标。为了确定和识别这类预测数学模型的参数结构,我们需要对所有这些坐标之间的相互作用进行实验研究的结果,由于这些坐标的原始形式是非平稳随机的,因此不能直接用于识别上述一类数学模型。在本文中,根据Vinnitsa国立医科大学获得的表征过敏性真菌孢子分布过程的所有这些非平稳随机坐标的实验研究结果进行以下处理,导致这些坐标的这些特征,然后可以用于预测数学模型的合成。这些特征是坐标的平稳分区,因为从它们的测量值到它们的增量和相关系数以及根据所有考虑的坐标的增量计算的自相关和相互相关函数的过渡。
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