The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling.

Background: Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources.

Materials And Methods: We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation.

Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan.

Background: Since the start of the COVID-19 pandemic, scientists have begun to actively use models to determine the epidemiological characteristics of the pathogen. The transmission rate, recovery rate and loss of immunity to the COVID-19 virus change over time and depend on many factors, such as the seasonality of pneumonia, mobility, testing frequency, the use of masks, the weather, social behavior, stress, public health measures, etc. Therefore, the aim of our study was to predict COVID-19 using a stochastic model based on the system dynamics approach.

Computation of nonlinear parameters of heart rhythm using short time ECG segments.

We propose the method to compute the nonlinear parameters of heart rhythm (correlation dimension D₂ and correlation entropy K₂) using 5-minute ECG recordings preferred for screening of population. Conversion of RR intervals' time series into continuous function x(t) allows getting the new time series with different sampling rate dt. It has been shown that for all dt (250, 200, 125, and 100 ms) the cross-plots of D₂ and K₂ against embedding dimension m for phase-space reconstruction start to level off at m = 9.