Data-Driven Decision-Making in Public Health: A Cross-Disciplinary Approach to Pandemic Response

Abstract

The COVID-19 pandemic has demonstrated the critical role of data science in public health decision-making. This paper explores how data-driven approaches, combined with insights from epidemiology, policy analysis, and behavioral science, can enhance pandemic response strategies. Using case studies from Australia and globally, we examine the use of predictive modeling, contact tracing algorithms, and real-time data dashboards. The paper concludes with recommendations for fostering interdisciplinary collaboration to improve data collection, analysis, and implementation in public health crises.