Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges
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,...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55461-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841544503798792192 |
|---|---|
| author | Yang Ye Abhishek Pandey Carolyn Bawden Dewan Md. Sumsuzzman Rimpi Rajput Affan Shoukat Burton H. Singer Seyed M. Moghadas Alison P. Galvani |
| author_facet | Yang Ye Abhishek Pandey Carolyn Bawden Dewan Md. Sumsuzzman Rimpi Rajput Affan Shoukat Burton H. Singer Seyed M. Moghadas Alison P. Galvani |
| author_sort | Yang Ye |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-41add67b580742ca8894b5a91c2d43fd |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-41add67b580742ca8894b5a91c2d43fd2025-01-12T12:30:17ZengNature PortfolioNature Communications2041-17232025-01-0116111810.1038/s41467-024-55461-xIntegrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challengesYang Ye0Abhishek Pandey1Carolyn Bawden2Dewan Md. Sumsuzzman3Rimpi Rajput4Affan Shoukat5Burton H. Singer6Seyed M. Moghadas7Alison P. Galvani8Center for Infectious Disease Modeling and Analysis, Yale School of Public HealthCenter for Infectious Disease Modeling and Analysis, Yale School of Public HealthDepartment of Microbiology and Immunology, McGill UniversityAgent-Based Modelling Laboratory, York UniversityCenter for Infectious Disease Modeling and Analysis, Yale School of Public HealthDepartment of Mathematics and Statistics, University of ReginaEmerging Pathogens Institute, University of FloridaAgent-Based Modelling Laboratory, York UniversityCenter for Infectious Disease Modeling and Analysis, Yale School of Public HealthAbstract 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.https://doi.org/10.1038/s41467-024-55461-x |
| spellingShingle | Yang Ye Abhishek Pandey Carolyn Bawden Dewan Md. Sumsuzzman Rimpi Rajput Affan Shoukat Burton H. Singer Seyed M. Moghadas Alison P. Galvani Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges Nature Communications |
| title | Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges |
| title_full | Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges |
| title_fullStr | Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges |
| title_full_unstemmed | Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges |
| title_short | Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges |
| title_sort | integrating artificial intelligence with mechanistic epidemiological modeling a scoping review of opportunities and challenges |
| url | https://doi.org/10.1038/s41467-024-55461-x |
| work_keys_str_mv | AT yangye integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT abhishekpandey integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT carolynbawden integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT dewanmdsumsuzzman integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT rimpirajput integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT affanshoukat integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT burtonhsinger integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT seyedmmoghadas integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges AT alisonpgalvani integratingartificialintelligencewithmechanisticepidemiologicalmodelingascopingreviewofopportunitiesandchallenges |