Potential of Artificial Intelligence in Detection, Management, and Prevention of Viral Diseases

Artificial intelligence (AI) is transforming the field of virology by enhancing the detection, management, and prevention of viral diseases, while also enabling early epidemic prediction. By integrating diverse types of data—ranging from demographic information to climate conditions and historical outbreak records—AI uncovers patterns that traditional methodologies might overlook. Machine learning (ML) models, trained on genomic data, medical imaging, and biomarker information, enhance both the speed and accuracy of diagnostics. Advanced AI technologies accurately identify pathogens and enable real-time detection of antiviral resistance. Furthermore, AI personalizes treatment plans by analyzing patients' genomic and health data, predicting drug efficacies, and adjusting dosage levels via wearable devices. For example, climate-informed models such as BlueDot can predict outbreaks weeks before they occur, utilizing Long Short-Term Memory (LSTM) and graph networks for support. AI-driven genomic surveillance assists in updating vaccines, while mobility data boosts contact tracing efforts. Despite facing challenges such as bias, privacy, and ethical concerns, interdisciplinary collaboration is essential. AI's ability to synthesize data from multiple sources is crucial for precision-based viral disease control.
This Research Topic aims to harness the power of artificial intelligence (AI) to revolutionize the detection, management, and prevention of viral diseases by integrating diverse datasets, including demographic, climatic, and historical outbreak records. The goal is to develop predictive models that can analyze these multi-source datasets—including genomic sequences, climate trends, and population mobility—to forecast viral epidemics with greater accuracy and advance lead times compared to current methodologies. AI-driven tools like BlueDot have successfully demonstrated early outbreak prediction by analyzing aviation and zoonotic data, while hospital-based systems such as EDS-HAT have reduced infections by 40% through the real-time analysis of electronic health records. This research intends to create adaptive frameworks that identify emerging threats through platforms like social media and genomic surveillance, optimize resource allocation during outbreaks, and personalize prevention strategies based on climate-driven habitat shifts. Interdisciplinary collaboration will be prioritized to address algorithmic bias, data privacy, and model interpretability, all while aligning with global health equity principles. By merging AI innovation with epidemiological expertise, the aim is to move from reactive toward proactive viral disease control. Contributions to advance this vision are encouraged, through shared datasets, computational resources, and ethical AI implementation.
To gather further insights in the scope of AI's potential in viral disease management and prevention, we welcome articles addressing, but not limited to, the following themes:
o Epidemic prediction: Development of AI models that integrate multiple datasets to forecast viral transmission.
o Genomic surveillance: Implementation of AI to track viral mutations and predict antigenic drift in real-time.
o Early detection systems: AI-based tools focusing on analyzing medical imaging, biomarker profiles, and social media signals for pre-symptomatic case identification.
o Zoonotic risk mapping: Utilization of satellite imagery, wildlife migration data, and AI to anticipate viral spillover hotspots originating from animal reservoirs.
o Vaccine development: Employing AI to enhance and expedite epitope prediction and the screening of vaccine candidates against emerging viruses.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Epidemic prediction, Viral disease surveillance, Early detection system, Variant tracking, Risk Factor Identification

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.