The monitoring, analysis and prediction of epidemic spread in the region require the construction of mathematical model, big data processing and visualization because the amount of population and the size of the region could be huge. One of the important steps is refinement of mathematical model, i.e. determination of initial data and coefficients of system of differential equations of epidemiologic processes using additional information. We analyze numerical method for solving inverse problem of epidemiology based on genetic algorithm and traditional optimization approach. Our algorithms are applied to analysis and prediction of epidemic situation in regions of Russian Federation, Republic of Kazakhstan and People’s Republic of China. Due to a great amount of data we use a special software ”Digital Earth” for visualization of epidemic.
Increasing image contrast is very important for the visual analysis of X-ray images. To improve the contrast of medical images, various contrast enhancement methods are used, such as histogram equalization and histogram modifications, gamma correction, etc. The paper explores adaptive methods for enhancing the contrast of digital X-ray images. Research was carried out on 1000 images from the open Kaggle database. Combinations of sequential application of several methods for enhancing image contrast were evaluated. Experiments using gamma image correction allowed us to select ranges of input and output parameters of the brightness conversion function. To obtain a better result, before performing gamma correction, it is proposed to use the method of equalizing the histogram of an X-ray image. Possibilities of adaptive image histogram equalization are explored. The performed experiments allow us to propose an improved version of increasing the contrast of X-ray images. Combining the adaptive histogram equalization algorithm with contrast clipping has a visually noticeable effect of improving the contrast of X-ray images. Contrast improvement is supported by objective NIQE and BRISQUE quantifications that do not require reference images. A feature of this work is the use of objective non-reference assessments to determine the quality of images. The performed experiments indicate that the NIQE score correlates better with the visual assessment of image contrast changes. As a result of the experiments, recommendations were proposed for choosing the parameters of the gamma correction and adaptive histogram equalization methods, which make it possible to enhance the contrast without the intensification of noise in the image
The article is devoted to the problem of using mathematical methods for forecasting the tuberculosis epidemic in Kazakhstan using the example of the Karaganda region. The introduction of the article reflects the problem of forecasting tuberculosis in Kazakhstan. The main part of the article reflects the results of the analysis of mathematical methods for forecasting and data processing technology, describes the mathematical analysis of epidemiological indicators made with the SPSS statistical program, describes the factors affecting the incidence among contact persons, and calculates the correlation coefficient. The article shows the importance of mathematical modeling and the importance of developing a specific mathematical model that describes the spread of infection among the population.
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