ArticleNature communications2025
Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Temporal epidemiology and multi-source forecasting of hemorrhagic fever with renal syndrome and leptospirosis in mainland China: An interpretable machine learning study.Preventive medicine reports · 2026Article
- Leveraging AI for infectious disease modelling and public health decision making.BMC proceedings · 2026Article
- [Transformation of Epidemiology in the Age of Artificial Intelligence].Revista medica del Instituto Mexicano del Seguro Social · 2026Article
- Spatio-temporal forecasting of dengue in the Americas through hybrid mechanistic and data-driven models: Systematic review and meta-analysis.Infectious Disease Modelling · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Machine Learning-Based Severity Stratification for Smart Preventive Decision Support: Evidence from Measles Surveillance in a Resource-Constrained Region.Journal of clinical medicine · 2026Article
- Interactys-AI: Toward AI-Driven Structural Mapping of Virus-Host Interfaces for Antiviral Repurposing and Pandemic Preparedness.Biomolecules · 2026Review
- Is artificial intelligence a friend or foe to epidemiology?Annals of epidemiology · 2026Review
- Survey on mathematical modeling of infectious disease dynamics: insights and applications.BMC infectious diseases · 2026Review
- Article
- The evolution of sampling in epidemiology: from classical probability models to AI-enhanced recruitment and active learning.Epidemiology and health · 2026Review
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.Reviews in medical virology · 2026Review
- Advancing epidemic intelligence: evaluating Senegal's mpox surveillance system and readiness for AI-driven predictive modelling.Frontiers in public health · 2026Article
- Training the next-generation of biomedical scientists through artificial intelligence-driven education and research in pharmacology and pharmaceutical sciences.Experimental biology and medicine (Maywood, N.J.) · 2026Review
- The neurotoxic legacy of CAR-T cells: where do we stand?Therapeutic advances in neurological disorders · 2026Review
- Systematic Determinants of Global COVID-19 Burden: Longitudinal Time-Series Analysis Using Big Data-Driven Artificial Intelligence.Journal of medical Internet research · 2025Article
- Modelling the Variability in Immunity Build-Up and Waning Following RNA-Based Vaccination.Viruses · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.