Kwok Kin On, Huynh Tom, Wei Wan In, Wong Samuel Y S, Riley Steven, Tang Arthur
JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China.
Comput Struct Biotechnol J. 2024 Aug 8;23:3254-3257. doi: 10.1016/j.csbj.2024.08.006. eCollection 2024 Dec.
OpenAI's ChatGPT, a Large Language Model (LLM), is a powerful tool across domains, designed for text and code generation, fostering collaboration, especially in public health. Investigating the role of this advanced LLM chatbot in assisting public health practitioners in shaping disease transmission models to inform infection control strategies, marks a new era in infectious disease epidemiology research. This study used a case study to illustrate how ChatGPT collaborates with a public health practitioner in co-designing a mathematical transmission model.
Using natural conversation, the practitioner initiated a dialogue involving an iterative process of code generation, refinement, and debugging with ChatGPT to develop a model to fit 10 days of prevalence data to estimate two key epidemiological parameters: i) basic reproductive number (Ro) and ii) final epidemic size. Verification and validation processes are conducted to ensure the accuracy and functionality of the final model.
ChatGPT developed a validated transmission model which replicated the epidemic curve and gave estimates of Ro of 4.19 (95 % CI: 4.13- 4.26) and a final epidemic size of 98.3 % of the population within 60 days. It highlighted the advantages of using maximum likelihood estimation with Poisson distribution over least squares method.
Integration of LLM in medical research accelerates model development, reducing technical barriers for health practitioners, democratizing access to advanced modeling and potentially enhancing pandemic preparedness globally, particularly in resource-constrained populations.
OpenAI的ChatGPT是一种大型语言模型（LLM），是跨领域的强大工具，用于文本和代码生成，促进协作，尤其是在公共卫生领域。研究这种先进的大型语言模型聊天机器人在协助公共卫生从业者构建疾病传播模型以指导感染控制策略方面的作用，标志着传染病流行病学研究的一个新时代。本研究采用案例研究来说明ChatGPT如何与公共卫生从业者合作共同设计一个数学传播模型。
通过自然对话，从业者发起了一个对话，涉及与ChatGPT进行代码生成、完善和调试的迭代过程，以开发一个模型，使其拟合10天的流行数据，以估计两个关键的流行病学参数：i）基本再生数（Ro）和ii）最终流行规模。进行了验证和确认过程，以确保最终模型的准确性和功能。
ChatGPT开发了一个经过验证的传播模型，该模型复制了疫情曲线，并给出了Ro的估计值为4.19（95%置信区间：4.13 - 4.26），以及在60天内最终流行规模为98.3%的人口。它突出了使用泊松分布的最大似然估计优于最小二乘法的优势。
在医学研究中整合大型语言模型可加速模型开发，减少卫生从业者的技术障碍，使先进建模的获取更加普及，并有可能在全球范围内加强大流行防范，特别是在资源有限的人群中。
