Data science in public health: A review of predictive analytics for disease control in the USA and Africa

Abstract

This scholarly paper delves into the realm of data science in public health, with a specific focus on the transformative role of predictive analytics in disease control across the United States and Africa. Set against a backdrop of rapidly evolving healthcare challenges, the study aims to dissect and synthesize the advancements, applications, and hurdles associated with data-driven health strategies in these diverse geographical contexts. Employing a qualitative analysis of peer-reviewed literature, the paper meticulously examines the evolution of predictive analytics, comparing public health structures, and scrutinizing key diseases and health challenges prevalent in both regions. The scope of the study extends to exploring the ethical considerations and technological advancements in health data utilization, offering a panoramic view of the current and potential landscape of data science in public health. The findings reveal a significant surge in the application of predictive analytics, particularly in the USA for chronic disease management and in Africa for infectious disease control. The study highlights the successes and challenges in implementing data-driven health policies, emphasizing the need for a balanced approach that addresses technological, ethical, and cultural barriers. The future of AI and machine learning in disease control is identified as a promising domain, with potential for further innovation and integration into healthcare and public policy. Conclusively, the paper recommends continued investment in data science applications in public health, advocating for collaborative efforts to overcome implementation challenges and ethical considerations. The study underscores the transformative potential of data science in enhancing healthcare delivery, advocating for more effective, efficient, and equitable healthcare systems globally.