Stochastic Models in the Problems of Predicting the Epidemiological Situation.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Stochastic Processes

Background:

• Accurate prediction of infectious disease spread is crucial for public health interventions.
• Existing models may not fully capture the complex dynamics of epidemics.
• Stochastic models offer a framework to incorporate randomness and uncertainty in disease transmission.

Purpose of the Study:

• To develop and evaluate stochastic models for predicting epidemiological disease spread.
• To identify key factors and parameters influencing epidemic forecasting.
• To provide a robust methodology for assessing disease transmission dynamics.

Main Methods:

• Investigation of stochastic models in both discrete and continuous time.
• Incorporation of various factors affecting epidemic spread.
• Parameter estimation and model validation through test calculations.

Main Results:

• Proposed stochastic models demonstrate utility in predicting disease spread.
• Identification of critical parameters impacting forecast accuracy.
• Successful application of methods in preliminary test calculations.

Conclusions:

• Stochastic modeling provides a valuable approach for epidemiological forecasting.
• Consideration of diverse factors enhances prediction reliability.
• The developed methods offer a foundation for future epidemic preparedness and response.