Combating Antibiotic Resistance: Leveraging Big Data for Predictive Global Surveillance

Abstract

Antibiotic resistance (AR) presents an escalating global health threat, projected to cause 10 million deaths annually by 2050 if left unaddressed. Traditional surveillance systems have struggled to provide timely and actionable data to combat AR due to fragmented data sources and limited global coordination. This study proposes the integration of big data analytics for predictive global surveillance of antibiotic resistance, leveraging multi-source datasets including electronic health records (EHRs), microbial genomics, pharmaceutical consumption databases, and environmental monitoring. We designed a scalable analytical pipeline incorporating machine learning algorithms, geospatial modeling, and time-series forecasting to identify AR hotspots and predict resistance trends. Results demonstrate enhanced sensitivity and predictive accuracy in identifying emerging resistance strains, surpassing conventional methods in both scale and resolution. Our findings highlight the transformative potential of big data to inform public health interventions, policy development, and clinical decision-making in real-time. This paper contributes a novel framework for global AR surveillance, encouraging the harmonization of data-driven health initiatives across international borders.