CURRENT ISSUE

ABSTRACT: Introduction: Thyroid cancer is one of the most common endocrine malignancies. According to global studies, its prevalence has been increasing worldwide and continues to grow. Although there have been global epidemiological studies on thyroid cancer, there is limited data on its epidemiology in Central Asian countries, including Kazakhstan.
Materials and Methods: A retrospective study utilized data from the Unified National Electronic Health System on thyroid cancer patients in Kazakhstan from 2014 to 2021. A descriptive analysis of patients was performed based on key demographic factors. Survival analysis was conducted using the Kaplan-Meier estimator and Cox proportional hazards regression.
Results: In total, 4,877 cases of thyroid cancer have been included during the period from 2014 to 2021 in Kazakhstan. Most of the diagnosed patients throughout the given period were females. The highest incidence and prevalence rates were found in the age group of 51-70 years old, while the highest mortality rate was among patients older than 70 years. Increasing age and male sex were the major predictors of mortality among thyroid cancer patients.
Conclusion: The obtained data coaligned with global data on thyroid cancer. Increased age, male sex, and living area were associated with poor prognosis. The study was limited by missing information on comorbidities and treatment types that the patients could have received. Therefore, further research is needed to assess the impact of such significant factors on survival.

ABSTRACT: Infectious disease surveillance has long been vital in public health, but traditional methods often fall short in detecting emerging threats and understanding pathogen evolution. Recent advances in Next-Generation Sequencing (NGS) have revolutionized genomic surveillance, enabling near real-time monitoring of pathogens at the genetic level. This study explores the integration of real-time genomic surveillance with epidemiological models to enhance disease intervention planning. We examine how combining genomic data with models like Susceptible-Infectious-Recovered (SIR) and Susceptible-Exposed-Infectious-Recovered (SEIR) improves outbreak forecasting, facilitates early detection of new variants, and provides actionable insights for targeted interventions. The integration of NGS data allows for more precise transmission network mapping, better-informed resource allocation, and dynamic policy adjustments. However, challenges persist, including technical limitations, data privacy concerns, and equity in global surveillance capacities. The findings suggest that genomic integration enhances epidemic prediction and response but requires robust policy frameworks, equitable data-sharing practices, and continuous capacity-building efforts in low- and middle-income regions. The future of infectious disease control hinges on advancing technologies like artificial intelligence (AI), cloud computing, and machine learning to improve predictive accuracy and support real-time decision-making. This review underscores the potential of genomic surveillance to transform public health strategies and outlines key steps for effective global collaboration.