AI-driven epidemic intelligence: the future of outbreak detection and response Article Swipe
Jasleen Kaur
,
Zahid A Butt
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/frai.2025.1645467
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/frai.2025.1645467
Epidemic intelligence, the process of detecting, verifying, and analyzing public health threats to enable timely responses, traditionally relies heavily on manual reporting and structured data, often causing delays and coverage gaps. The growing frequency of emerging infectious diseases highlights the urgency for more rapid and accurate surveillance methods. This perspective proposes a forward-looking conceptual framework for AI-driven epidemic intelligence, emphasizing the transformative potential of integrating large language models (LLMs), natural language processing (NLP), and optimization-based resource allocation strategies. While existing AI-driven systems have shown significant capabilities during the COVID-19 pandemic, several challenges remain, including real-time adaptability, multilingual data handling, misinformation, and public health policy alignment. To address these gaps, we propose an integrated, real-time adaptable LLM-based epidemic intelligence system, capable of correlating cross-source data, optimizing healthcare resource allocation, and supporting informed outbreak response. This approach aims to significantly improve early warning capabilities, enhancing forecasting accuracy, and strengthen pandemic preparedness.
Related Topics
Concepts
Computer science
Preparedness
Transformative learning
Pandemic
Data science
Process (computing)
Risk analysis (engineering)
Computer security
Infectious disease (medical specialty)
Coronavirus disease 2019 (COVID-19)
Business
Medicine
Political science
Psychology
Disease
Pedagogy
Pathology
Law
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/frai.2025.1645467
- https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1645467/pdf
- OA Status
- gold
- Cited By
- 3
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412765880
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412765880Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/frai.2025.1645467Digital Object Identifier
- Title
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AI-driven epidemic intelligence: the future of outbreak detection and responseWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-30Full publication date if available
- Authors
-
Jasleen Kaur, Zahid A ButtList of authors in order
- Landing page
-
https://doi.org/10.3389/frai.2025.1645467Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1645467/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1645467/pdfDirect OA link when available
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Computer science, Preparedness, Transformative learning, Pandemic, Data science, Process (computing), Risk analysis (engineering), Computer security, Infectious disease (medical specialty), Coronavirus disease 2019 (COVID-19), Business, Medicine, Political science, Psychology, Disease, Pedagogy, Pathology, Law, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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