The Role of Big Data in Combatting Antibiotic Resistance Predictive Models for Global Surveillance

Abstract

The worldwide health emergency of antibiotic resistance makes medical treatments less effective and leads to higher death statistics. The widespread problem of antibiotic resistance stems from the extreme and improper use of antibiotics in medical practices, livestock operations, and agricultural farms. Big data analytics integration serves as an innovative method to predict, monitor, and reduce antibiotic resistance by implementing big data analytics systems. This research adopts a methodical approach to scrutinize the WHO Global Antimicrobial Resistance and Use Surveillance System alongside different national healthcare records available to the public. The assessment of resistance trends region-based predictions and outbreak forecasts is performed using machine learning algorithms with supporting artificial intelligence models. The prediction accuracy gets boosted the application of regression analysis, clustering and neural networks as statistical methods. The evaluation section of the study demonstrates how big data performs in healthcare facilities to monitor systems and make on-the-spot decisions. The presented research demonstrates how big data maintains its essential position for the surveillance and early detection of antibiotic resistance. The predictive models reveal important patterns about antibiotic resistance, which helps leaders and healthcare experts with researchers, to create focused strategies to fight antimicrobial resistance. The current challenges involving data standardization  with privacy issues and real-time data access cannot hinder big data analytics from achieving substantial effects on fighting antibiotic resistance worldwide. Sustained development of artificial intelligence surveillance systems alongside multi-disciplinary relationships creates essential conditions to protect antibiotic effectiveness in the future.