Artificial Intelligence and Digital Health Innovations for Communicable Disease Surveillance, Prevention and Control

Communicable diseases (CDs) remain a primary global cause of morbidity and mortality, necessitating rigorous surveillance to manage outbreaks, healthcare-associated infections (HAIs), and antimicrobial resistance (AMR). Traditional public health strategies often face persistent hurdles, including fragmented surveillance systems, workforce shortages, and delayed detection. Recent advances in artificial intelligence (AI) and digital health offer transformative opportunities to strengthen communicable disease surveillance, prevention and control across healthcare and community settings. From real-time surveillance and predictive modelling to automated decision support, digital contact tracing, and advanced environmental monitoring, these tools are reshaping how infections are detected, prevented, and controlled.This Research Topic aims to address the gap between the potential of digital innovations and their practical, ethical implementation in diverse real-world settings. We aim to showcase interdisciplinary research that utilizes AI-driven tools to enhance health system resilience and evidence-based decision-making. Key objectives include evaluating the impact of automated decision support, optimizing resource allocation through predictive modelling, and addressing critical challenges such as data quality, interpretability, and global health equity. We seek to advance scalable, context-sensitive solutions that strengthen both healthcare-based and population-level actions.We particularly welcome contributions addressing, but not limited to, the following themes:• AI-Driven Communicable Disease Surveillance: Real-time outbreak detection using clinical, laboratory, genomic, environmental, mobility or digital trace data; early warning systems for epidemic and pandemic preparedness; automated AI-based surveillance for structured and unstructured health records. • Predictive Modelling and Decision Support: Communicable disease transmission and burden modelling; AI-assisted decision-making and resource allocation; risk factor analyses based on machine learning.• Digital Tools for Prevention and Response: Digital applications supporting vaccination development, delivery and coverage monitoring; digital contact tracing and exposure notification systems; tools for risk communication, infodemic management and community engagement.• Digital Innovations for Infection Prevention and Control: AI-supported monitoring of infection prevention and control practices; risk prediction for healthcare-associated infections occurrence and outcomes; integration of infection prevention and control data into surveillance systems.• Digital Innovations for Antimicrobial Resistance: Predictive analytics for resistance patterns; decision support for antimicrobial stewardship; automated resistance for phenotypic or genotypic detection in the laboratory.• Equity, Ethics and Governance for Communicable Disease Prevention and Control: Bias, fairness and explainability of AI models and human-AI collaboration; data privacy, security and regulatory frameworks; implementation science, health system integration and workforce training. Topic editors: Marcello Di Pumpo, Francesco Baglivo, Silvana Gastaldi, Tommi Kärki, Costanza Vicentini Source: https://www.frontiersin.org/research-topics/77731/artificial-intelligence-and-digital-health-innovations-for-communicable-disease-surveillance-prevention-and-control