APPLYING BIOSTATISTICAL METHODS TO ADDRESS INEQUALITIES IN INFECTIOUS DISEASE OUTCOMES

Abstract

This review explores emerging biostatistical methods, the integration of machine learning (ML) and advanced analytics, and the role of big data and artificial intelligence (AI) in addressing health disparities in public health. It highlights the growing importance of Bayesian models and ML algorithms for predicting infectious disease outcomes and stratifying populations by social determinants of health. The review accentuates the potential of AI in precision public health, with applications ranging from real-time disease surveillance to the development of personalized interventions. However, it also emphasizes the ethical challenges and biases associated with AI and ML, particularly in marginalized populations. Future research recommendations focus on developing ethical frameworks, improving the representativeness of training data, and optimizing the use of real-world evidence (RWE) in public health. By combining traditional biostatistical approaches with modern AIdriven tools, this review outlines a path toward more accurate and equitable health outcome predictions, ultimately contributing to the reduction of health disparities on a global scale

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