Geo-information system of spread of tuberculosis based on inversion and prediction

TL;DR

This paper develops a geo-information system utilizing inversion and prediction techniques to analyze and forecast tuberculosis spread, integrating big data processing, mathematical modeling, and visualization for large regions.

Contribution

It introduces a novel approach combining genetic algorithms and traditional optimization for epidemiological inverse problems within a geo-information framework.

Findings

Effective prediction of epidemic spread in large regions.

Successful application to regions in Russia, Kazakhstan, and China.

Enhanced visualization of epidemic data using Digital Earth software.

Abstract

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 which describe the epidemiology processes. We analyze numerical method for solving inverse problem of epidemiology based on genetic algorithm and traditional optimization ideas. Numerical results 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 Geo-information system for visualization of epidemic process, i.e. a special software named Digital Earth.