Application of generative artificial intelligence for epidemic-based modelling
Epidemic models have become increasingly essential, especially in the wake of the recent COVID-19 pandemic, emphasising the crucial role of human behaviour in the spread of disease. There has been a recent rise in the usage and popularity of Generative Artificial Intelligence (AI), such as ChatGP...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/174974 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Epidemic models have become increasingly essential, especially in the wake of the recent
COVID-19 pandemic, emphasising the crucial role of human behaviour in the spread of disease.
There has been a recent rise in the usage and popularity of Generative Artificial Intelligence
(AI), such as ChatGPT especially with its ability to mimic human behaviour. This final year
project explores the novel application of Generative AI, aiming to overcome the challenge of
incorporating nuanced human behaviour in epidemic models. By leveraging ChatGPT, this study
seeks to enhance the cognitive realism of agents within an Agent-Based Model (ABM). In this
approach, each agent’s actions and behaviour are decided by ChatGPT through a behaviour grid.
Various experiments were conducted with different contexts revealing that the simulations
successfully generated Susceptible-Infected-Recovered (SIR) graphs and social contact matrices
that closely mimic observed epidemic patterns. Furthermore, this project showed the potential of
using Random Forests in complementing ChatGPT data to identify the most influential factors in
its decision-making. However, the project also highlights the double-edged nature of prompt
engineering in ChatGPT, highlighting its sensitivity. The findings underscore the need for careful
consideration and refinement in utilising generative AI for modelling complex systems. This
project not only contributes to the advancement of epidemic modelling but also underscores the
versatility of generative AI in enhancing the cognitive realism of agent-based simulations. As the
world grapples with the aftermath of a global pandemic, the imperative for robust epidemic
models becomes more pronounced than ever, and the integration of advanced technologies like
Generative AI becomes pivotal in addressing future health crises. |
|---|