Leveraging big data and analytics for enhanced public health decision-making: A global review

In recent years, the proliferation of big data and analytics technologies has revolutionized various sectors, including public health. This review presents a comprehensive review of how leveraging big data and analytics has enhanced public health decision-making on a global scale. The review encompasses diverse applications, methodologies, challenges, and opportunities within this burgeoning field. Big data analytics in public health encompasses the collection, processing, and analysis of vast datasets from heterogeneous sources, including electronic health records, social media, wearable devices, and environmental sensors. These data sources offer valuable insights into disease patterns, risk factors, healthcare utilization, and population health trends. By applying advanced analytical techniques such as machine learning, predictive modeling, and data visualization, public health officials can extract actionable intelligence to inform decision-making processes. Several case studies highlight the efficacy of big data analytics in various public health domains. For instance, predictive modeling techniques have been utilized to forecast disease outbreaks, enabling timely resource allocation and intervention planning. Social media mining has facilitated real-time surveillance of public sentiment and health-related behaviors, aiding in targeted health promotion campaigns. Additionally, electronic health record analysis has facilitated personalized medicine initiatives and improved patient outcomes. Despite the significant potential of big data analytics in public health, several challenges exist. These include data privacy concerns, data quality issues, interoperability barriers, and the digital divide. Furthermore, ethical considerations regarding consent, transparency, and equity must be carefully addressed to mitigate potential biases and ensure responsible data usage. Looking ahead, the future of leveraging big data and analytics for public health decision-making appears promising. Advancements in data integration, interoperability standards, and artificial intelligence hold immense potential for improving the accuracy, timeliness, and relevance of public health interventions. Collaborative efforts between governments, academia, industry, and civil society are essential to harness the full potential of big data analytics in safeguarding population health and promoting well-being on a global scale.