Nonparametric Analysis of Tracking Data in the Context of COVID-19 Pandemic

Klyushin, D. A.

Methods of statistical pattern recognition are powerful tools for analysis of statistical data on COVID-19 pandemic. In this chapter, we offer a new effective online algorithm for detection of change-points in tracking data (movement data, health rate data etc.). We developed a non-parametric test for evaluation of the statistical hypothesis that data in two adjacent time intervals have the same distribution. In the context of the mobile phone tracking this means that the coordinates of the tracked object does not deviated from the base point significantly. For estimation of the health rate it means the absence of significant deviations from the norm. The significance level for the test is less than 0.05. The test permits ties in samples. Also, we show that results of comparison of the test with well-known Kolmogorov–Smirnov test. These results show that the proposed test is more robust, sensitive and accurate than the alternative ones. In addition, the new method does not require high computational capacity.