Publications by authors named "S N Kadyrov"

Mathematical modeling plays a crucial role in understanding and combating infectious diseases, offering predictive insights into disease spread and the impact of vaccination strategies. This paper explored the significance of mathematical modeling in epidemic control efforts, focusing on the interplay between vaccination strategies, disease transmission rates, and population immunity. To facilitate meaningful comparisons of vaccination strategies, we maintained a consistent framework by fixing the vaccination capacity to vary from 10 to 100% of the total population.

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Tuberculosis (TB) is a highly contagious disease that remains a global concern affecting numerous countries. Kazakhstan has been facing considerable challenges in TB prevention and treatment for decades. This study aims to understand TB transmission dynamics by developing and comparing statistical and deterministic models: Seasonal Autoregressive Integrated Moving Average (SARIMA) and the basic Susceptible-Infected-Recovered (SIR).

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Background: Treatment of patients with prolonged and permanent disturbance of consciousness is still an extremely difficult problem. Nowadays, management is based on pathophysiological and molecular mechanisms of impaired consciousness. Several electrophysiological and pharmacological methods were proposed to restore consciousness in appropriate patients.

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Unlabelled: An urgent problem in modern neurosurgery is resection of brain tumors adjacent to corticospinal tract (CST) due to high risk of its damage and subsequent disability. The main methods for prevention of intraoperative damage to CST are preoperative MR tractography and intraoperative electrophysiological monitoring. Both methods are used in pediatric neurosurgery.

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Article Synopsis
  • - This study examines the growing HIV crisis in Kazakhstan, aiming to predict infection prevalence from 2020 to 2030 using mathematical modeling and time series analysis methods.
  • - Two models, ARIMA and the Susceptible-Infected (SI) model, forecast an increase in HIV prevalence, with the SI model predicting a higher rate by 2030, while also evaluating the impact of pre-exposure prophylaxis (PrEP) on reducing rates.
  • - The findings suggest that both models are statistically significant and recommend their use for healthcare planning and resource allocation to effectively combat HIV in the region.
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