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The integration of data analytics into public health practices represents a transformative paradigm shift in the United States. This review provides a comprehensive analysis of the impact and implications of data analytics on public health strategies, with a focus on disease surveillance and health policy within the USA. In the context of disease surveillance, data analytics has emerged as a crucial tool for real-time monitoring and early detection of health threats. Leveraging diverse datasets, including electronic health records and social media, allows for swift identification of trends and anomalies, enabling proactive responses to potential outbreaks. Advanced analytics techniques, such as machine learning and predictive modeling, contribute to the precision of surveillance efforts, facilitating targeted interventions and resource allocation. Beyond disease surveillance, data analytics significantly influences health policy. Evidence-based policy formulation is enhanced through data-driven insights, providing policymakers with a foundation for understanding the impact of interventions and designing strategies that align with the unique needs of diverse populations. Resource allocation strategies are optimized, ensuring efficient use of limited resources by analyzing health outcomes, service utilization patterns, and cost-effectiveness. Continuous monitoring and evaluation of implemented health policies through data analytics enable policymakers to adapt strategies in response to evolving health challenges, fostering a dynamic and adaptive public health ecosystem. As the landscape of public health evolves, data analytics in the USA continues to play a central role in shaping strategies and policies. The study delves into the historical context, key components, applications, and success stories, providing valuable insights for policymakers, public health professionals, and researchers aiming to navigate the complexities of data-driven public health management.
Babarinde et al. (Mon,) studied this question.