STI and HIV Prevention Through Public Health Digital Twins: A Framework for Personalized Prevention and Adaptive Disease Intervention

Abstract

Sexually transmitted infections (STIs) and Human Immunodeficiency Virus (HIV) continue to present significant public health challenges worldwide, necessitating innovative prevention approaches that integrate advanced digital technologies with personalized and adaptive intervention strategies. This study investigated the influence of Public Health Digital Twin Capability on STI and HIV Prevention Performance through the mediating effects of Personalized Prevention Effectiveness and Adaptive Intervention Capacity. A quantitative cross-sectional research design was employed, and data were collected from 412 public health professionals, epidemiologists, infectious disease specialists, healthcare administrators, health informatics experts, and STI/HIV program coordinators. The conceptual framework was developed based on Digital Twin Theory, Precision Public Health, and Adaptive Intervention Theory. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 and SPSS 29. The findings revealed that Public Health Digital Twin Capability exerted significant positive effects on Personalized Prevention Effectiveness (β = 0.712, p < 0.001), Adaptive Intervention Capacity (β = 0.684, p < 0.001), and STI and HIV Prevention Performance (β = 0.276, p < 0.001). Personalized Prevention Effectiveness (β = 0.358, p < 0.001) and Adaptive Intervention Capacity (β = 0.412, p < 0.001) also demonstrated significant positive effects on prevention performance. Mediation analysis confirmed significant indirect effects through Personalized Prevention Effectiveness (β = 0.255, p < 0.001) and Adaptive Intervention Capacity (β = 0.282, p < 0.001), with both mediators jointly accounting for 66.06% of the total effect. The structural model explained 69.4% of the variance in STI and HIV Prevention Performance (R² = 0.694), indicating substantial explanatory power. Secondary analyses further identified Predictive Analytics Capability (r = 0.748, p < 0.001) and Intervention Responsiveness (r = 0.756, p < 0.001) as the strongest correlates of prevention performance.