Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges
Yale University · York University · +3 more institutions
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…
Citation impact
- FWCI
- 83.12
- Percentile
- 100%
- References
- 295
Authors
9Topics & keywords
- Computer science
- Data science
- Management science
- Transformative learning
- Artificial intelligence
- Risk analysis (engineering)
- Medicine
- Engineering