ArticleComputational and structural biotechnology journal2024
Utilizing large language models in infectious disease transmission modelling for public health preparedness.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Large language models as versatile predictive engines for notifiable infectious diseases.PLOS digital health · 2026Article
- Evidence-based One Health strategy is essential to eliminate rabies by 2030.Annals of medicine and surgery (2012) · 2026Article
- Development and Evaluation of a Generative AI Chatbot for Database Searching in Systematic Review.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2026Article
- Rebalancing power in infectious disease modelling: Toward inclusive and contextual approaches.PLOS global public health · 2026Article
- Large Language Models in Action: Supporting Clinical Evaluation in an Infectious Disease Unit.Healthcare (Basel, Switzerland) · 2025Article
- Comparing new tools of artificial intelligence to the authentic intelligence of our global health students.BioData mining · 2024Article
- Harnessing computational tools of the digital era for enhanced infection control.BMC medical informatics and decision making · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.