A Holistic Framework for Leveraging Big Data Analytics and AI to Influence Public Health Policies through IoT-Based Water Monitoring

Water quality is a critical determinant of public health, influencing the prevalence of waterborne diseases and overall community well-being. This paper proposes a holistic framework that integrates big data analytics, artificial intelligence (AI), and Internet of Things (IoT)-based water monitoring to inform and enhance public health policies. IoT devices provide real-time, high-resolution data on water quality parameters such as pH, turbidity, and contaminants, while advanced analytics and AI enable predictive modeling, risk assessment, and evidence-based decision-making. The framework emphasizes strategies for embedding technology-driven insights into policymaking processes, addressing infrastructure gaps, data privacy, and resistance to change. Ethical and regulatory considerations, including equity, transparency, and algorithmic accountability, are explored to ensure responsible implementation. The potential impact of this approach is substantial, promising improved health outcomes, reduced disparities, and alignment with sustainability goals. Recommendations for implementation include capacity building, infrastructure investment, community participation, and ongoing evaluation. The paper concludes with directions for future research, underscoring the importance of innovation and interdisciplinary collaboration in advancing global water quality management.