In this section
@article{Kwok:2024:10.1016/j.csbj.2024.08.006,
author = {Kwok, KO and Huynh, T and Wei, WI and Wong, SYS and Riley, S and Tang, A},
doi = {10.1016/j.csbj.2024.08.006},
journal = {Computational and Structural Biotechnology Journal},
pages = {3254--3257},
title = {Utilizing large language models in infectious disease transmission modelling for public health preparedness},
url = {http://dx.doi.org/10.1016/j.csbj.2024.08.006},
volume = {23},
year = {2024}
}
TY - JOUR
AB - 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.
AU - Kwok,KO
AU - Huynh,T
AU - Wei,WI
AU - Wong,SYS
AU - Riley,S
AU - Tang,A
DO - 10.1016/j.csbj.2024.08.006
EP - 3257
PY - 2024///
SP - 3254
TI - Utilizing large language models in infectious disease transmission modelling for public health preparedness
T2 - Computational and Structural Biotechnology Journal
UR - http://dx.doi.org/10.1016/j.csbj.2024.08.006
VL - 23
ER -
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