A REVIEW OF STOCHASTIC MODELING TECHNIQUES IN PUBLIC HEALTH RISK ASSESSMENT AND POLICY DEVELOPMENT

Abstract

Stochastic modeling has emerged as a vital tool in public health risk assessment and policy development, enabling decision-makers to account for uncertainty and variability in epidemiological and environmental health studies. This review explores the application of stochastic modeling techniques in assessing public health risks and shaping evidence-based policies. It discusses key methodologies, including Monte Carlo simulations, Bayesian networks, Markov models, and agent-based modeling, highlighting their strengths and limitations in addressing complex health challenges. Monte Carlo simulations are widely used to quantify uncertainty in disease spread and exposure assessment, allowing policymakers to evaluate intervention strategies under different probabilistic scenarios. Bayesian networks facilitate probabilistic reasoning by integrating prior knowledge with real-time data, improving the accuracy of disease prediction models. Markov models, particularly in chronic disease progression studies, provide insights into long-term health outcomes and cost-effectiveness of interventions. Agent-based modeling is instrumental in understanding individual and population-level behaviors, particularly in the context of infectious disease transmission and health policy compliance. The review also examines real-world applications of stochastic models in epidemiological surveillance, vaccination strategies, and environmental exposure risk assessment. Notable case studies include their use in modeling COVID-19 transmission dynamics, optimizing influenza vaccination policies, and assessing the impact of air pollution on respiratory diseases. Additionally, the integration of stochastic approaches with machine learning and artificial intelligence is discussed, emphasizing their role in enhancing predictive analytics for public health. Despite their advantages, stochastic models face challenges such as computational complexity, data availability, and model validation. Addressing these limitations requires interdisciplinary collaboration, robust data collection frameworks, and advancements in computational power. The review underscores the importance of stochastic modeling in public health risk assessment and emphasizes the need for continued innovation to refine predictive accuracy and policy relevance. Future research directions should focus on improving model interpretability, incorporating real-time data streams, and developing hybrid models that combine stochastic and deterministic approaches. By providing a comprehensive review of stochastic modeling techniques, this study contributes to a deeper understanding of their applications in public health. It advocates for their broader adoption in policy formulation to enhance health system resilience and risk mitigation strategies.