Incorporating Large Language Models in Public Health Education - Project Summary

Description The goal of this assignment is to produce student generated epidemic models that can address a healthcare issue using—entirely—ChatGPT.

Abstract There is an immediate need for public health practitioners to incorporate mathematical modeling into infectious diseases decision making. This is demonstrated by a continued increase in the use of forecasting models by state and federal health agencies such as the CDC. However, the typical public health curriculum does not require mathematical modeling and programming of epidemic models. In my class, Outbreak Science and Public Health forecasting, one week will be devoted to the use of Large Language Models to construct epidemic models that can incorporate temporal public health decisions. The only restriction is that students ask the LLM to construct a compartmental model, a typical model used in infectious disease modeling. Students will be assessed on the feasibility of the model, the ability of the model to include practical public health decisions or interventions, and the student's ability to apply what they have learned in class to describe the model to others. In addition to the instructor, the Chief of the Division of Infectious Diseases and Chief Infection Control and Prevention Officer from the Lehigh Valley Health Network have agreed to evaluate student proposals. This exercise represents a paradigm shift in public health education and how LLM tools can be used to construct epidemic models that are impactful and improve evidence-based public health decision making.