Utilizing large language models in infectious disease transmission modelling for public health preparedness.

Autor:	Kwok KO; 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.; Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom., Huynh T; School of Science, Engineering and Technology, RMIT University, Viet Nam., Wei WI; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China., Wong SYS; JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China., Riley S; MRC Centre for Global Infectious Disease Analysis and Jameel Institute, Imperial College London, London, United Kingdom.; School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, United Kingdom., Tang A; School of Science, Engineering and Technology, RMIT University, Viet Nam.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 Aug 08; Vol. 23, pp. 3254-3257. Date of Electronic Publication: 2024 Aug 08 (Print Publication: 2024).
DOI:	10.1016/j.csbj.2024.08.006
Abstrakt:	Introduction: 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. Methods: 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. Results: 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. Conclusion: 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. Competing Interests: None. (© 2024 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)
Databáze:	MEDLINE
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