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Article: Epidemic PINNs: A Chatbot Based on Dual Data- and Physical-Driven Epidemic Prediction
| Title | Epidemic PINNs: A Chatbot Based on Dual Data- and Physical-Driven Epidemic Prediction |
|---|---|
| Authors | |
| Issue Date | 30-Sep-2025 |
| Publisher | IOS Press |
| Citation | Frontiers in Artificial Intelligence and Applications, 2025, v. 412, p. 275-290 How to Cite? |
| Abstract | In this project, a chatbot is designed to analyze and predict data collected during the COVID-19 pandemic in Australia, with a specific focus on Victoria. Data for Victoria are extracted from comprehensive datasets covering all Australian states, and the chatbot utilizes the susceptible-infectious-recovered (SIR) model and a physics-informed neural network (PINN) to forecast the future dynamics for specific population groups. In addition to its predictive capabilities, the chatbot offers in-depth insights obtained through data analysis and text analysis of the overall dataset. By integrating advanced modeling techniques with conversational AI, this project delivers accurate forecasts and actionable data-driven insights, which can support public health decision making and enhance user engagement. |
| Persistent Identifier | http://hdl.handle.net/10722/365943 |
| ISSN | 2023 SCImago Journal Rankings: 0.281 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Hu, Hanyu | - |
| dc.contributor.author | Hu, Shengdao | - |
| dc.contributor.author | Wei, Yihai | - |
| dc.contributor.author | Lau, Adela S.M. | - |
| dc.date.accessioned | 2025-11-12T00:36:40Z | - |
| dc.date.available | 2025-11-12T00:36:40Z | - |
| dc.date.issued | 2025-09-30 | - |
| dc.identifier.citation | Frontiers in Artificial Intelligence and Applications, 2025, v. 412, p. 275-290 | - |
| dc.identifier.issn | 0922-6389 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/365943 | - |
| dc.description.abstract | <p>In this project, a chatbot is designed to analyze and predict data collected during the COVID-19 pandemic in Australia, with a specific focus on Victoria. Data for Victoria are extracted from comprehensive datasets covering all Australian states, and the chatbot utilizes the susceptible-infectious-recovered (SIR) model and a physics-informed neural network (PINN) to forecast the future dynamics for specific population groups. In addition to its predictive capabilities, the chatbot offers in-depth insights obtained through data analysis and text analysis of the overall dataset. By integrating advanced modeling techniques with conversational AI, this project delivers accurate forecasts and actionable data-driven insights, which can support public health decision making and enhance user engagement.</p> | - |
| dc.language | eng | - |
| dc.publisher | IOS Press | - |
| dc.relation.ispartof | Frontiers in Artificial Intelligence and Applications | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.title | Epidemic PINNs: A Chatbot Based on Dual Data- and Physical-Driven Epidemic Prediction | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.3233/FAIA250728 | - |
| dc.identifier.volume | 412 | - |
| dc.identifier.spage | 275 | - |
| dc.identifier.epage | 290 | - |
| dc.identifier.eissn | 1535-6698 | - |
| dc.identifier.issnl | 0922-6389 | - |