Bayesian Decision-Making in Public Health Interventions in Uganda

Waiswa Micheal Anthony

Faculty of Business and Management Kampala International University Uganda

ABSTRACT

This article explores the application of Bayesian statistics in enhancing decision-making processes for public health interventions in Uganda. Bayesian methods offer a flexible framework that integrates prior knowledge, expert opinions, and real-time data to inform evidence-based strategies under uncertainty. The paper discusses the role of Bayesian statistics in disease modeling, highlighting its ability to improve predictive accuracy by incorporating historical data and epidemiological trends. It also examines how Bayesian decision-making optimizes resource allocation in Uganda’s healthcare system, emphasizing adaptive approaches to address varying disease burdens and resource constraints. Furthermore, the article explores Bayesian techniques for evaluating and adapting intervention strategies, demonstrating their effectiveness in guiding timely adjustments to maximize impact and cost-effectiveness. Through specific case studies and recent research examples, this article illustrates how Bayesian statistics contribute to shaping policies and improving population health outcomes in Uganda’s public health landscape.

Keywords: Bayesian statistics, Public health interventions, Uganda, Disease modeling, Adaptive strategies

CITE AS: Waiswa Micheal Anthony (2024). Bayesian Decision Making in Public Health Interventions in Uganda. IAA Journal of Arts and Humanities 11(2):46-48. https://doi.org/10.59298/IAAJAH/2024/11.4648.33