Smart Biosensors in Public Health Monitoring

Abstract

Smart biosensors that couple biological recognition elements with connected electronics and machine learning analytics promise to transform public health surveillance from retrospective laboratory reporting into real-time, distributed detection of pathogens, toxins, and biomarkers across populations, yet field deployment remains uneven across analyte categories and surveillance contexts. We evaluated 212 smart biosensor programmes deployed or piloted for public health monitoring across centres affiliated with the Mediterranean Institute of Technology in Rome between 2015 and 2024, spanning five application categories: waterborne pathogen detection, airborne respiratory virus surveillance, food safety contaminant screening, environmental toxin monitoring, and wastewater-based epidemiology. A Smart Biosensor Public Health Index (SBPHI) was constructed from five sub-scores -- analytical sensitivity and specificity, field robustness, data connectivity and reporting latency, population coverage scalability, and actionable alert generation -- with weights from regression against sustained programme operation beyond 12 months. SBPHI correlated with programme retention at r = +0.83 and discriminated retained from discontinued programmes with an AUC of 0.878. Wastewater-based epidemiology programmes scored highest (mean SBPHI 0.816), while environmental toxin monitoring trailed at 0.594. Only 34.0 percent of programmes exceeded the 0.75 SBPHI threshold. Analytical sensitivity and specificity carried the largest regression weight (beta = +0.278), followed by field robustness (beta = +0.228)