Leveraging Machine Learning for Effective Public Health Policies: Lessons from the COVID-19 Pandemic and Future Directions in Global Health

Abstract

The COVID-19 pandemic has exposed the vulnerabilities of global health systems and highlighted the need for rapid, data-driven decision-making in public health. Machine learning (ML) has the potential to provide valuable insights and contribute to improved health outcomes. In this paper, we aim to explore the role of ML in global health, identify its limitations, and discuss strategies to overcome these challenges while maximizing its benefits. We conduct a comprehensive literature review and analyze case studies from the COVID-19 pandemic. Our findings indicate that ML has played a significant role in the COVID-19 response, particularly in areas such as disease modeling, drug discovery, and resource allocation. However, several limitations, including data quality and accessibility, hinder the full potential of ML in global health. We propose strategies to overcome these limitations, such as promoting data-sharing collaborations, ensuring data privacy, and fostering interdisciplinary research. This paper contributes to the ongoing conversation on the applications and limitations of ML in global health, providing insights and recommendations for researchers, practitioners, and policymakers to effectively leverage ML for improved public health outcomes.

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