The field of infectious disease monitoring is facing unprecedented challenges due to the emergence of novel pathogens, the resurgence of endemic diseases, and the growing impact of climate-driven infectious diseases. Traditional surveillance systems often fall short, hindered by fragmented data, delayed response times, and a lack of interdisciplinary collaboration. However, recent advances in technology are paving the way for more integrated and responsive surveillance solutions. The convergence of artificial intelligence, multi-source data integration, and the One Health approach presents a transformative shift in how we monitor and respond to infectious threats. Expanding pathogen data has enhanced our understanding of transmission dynamics, while machine learning models are increasingly enhancing predictive capabilities. Despite these advances, substantial innovation and cross-sector collaboration are still required to close critical gaps and build truly resilient early warning systems.
This Research Topic aims to advance academic dialogue on AI and multi-source data in infectious disease surveillance. With growing challenges posed by emerging pathogens, climate-driven risks, and zoonotic threats, there is an urgent need to build dynamic, real-time early warning systems. This collection will focus on how AI-driven models can enhance disease prediction, how diverse data streams from clinical, environmental, animal health, and social sources can be effectively combined for robust risk mapping, and how the One Health approach can facilitate cross-sectoral data sharing. We invite contributions that explore innovative methodologies, applications, and frameworks that push the boundaries of traditional surveillance and support timely public health responses.
To gather further insights into enhancing surveillance methodologies, we welcome articles addressing, but not limited to, the following themes:
• Development and validation of innovative monitoring tools from novel data sources like wastewater surveillance and wearable devices.
• Advances in data fusion techniques for integrating spatiotemporal datasets and filtering noise from sources like social media.
• Applications of AI and machine learning for identifying high-risk populations and predicting climate-sensitive diseases.
• Surveillance strategies for emerging and re-emerging pathogens, with a focus on discovery and antimicrobial resistance.
• Design and implementation of collaborative One Health surveillance systems, including environmental pathogen tracking.
• Ethical considerations in surveillance technologies, incorporating privacy safeguards and digital equity.
• Pathogen evolution-dynamic surveillance coupling, real-time genomic surveillance for adaptive alert thresholds, multiscale modeling of antimicrobial resistance gene transmission, and virulence trajectory prediction based on evolutionary pressure.
• Climate-informed surveillance frameworks, including predictive mapping of vector habitat shifts, modeling of extreme weather–disease outbreaks, and risk assessment of ancient pathogen release from permafrost thaw.
This Research Topic seeks to build a forward-looking framework for next-generation infectious disease surveillance, providing core scientific evidence and strategies to bolster preparedness and response to public health threats.
Keywords: Intelligent Surveillance, Multisource Data Integration, Infectious Disease Monitoring, Early Warning Systems
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
