Join three public health practitioners for a workshop demonstrating practical applications of data automation, machine learning and AI that can be adapted for use in public health agencies.

The workshop will combine demonstrations with hands-on activities, through six practical case studies, helping participants build confidence to test and adopt appropriate solutions in their own workplaces.

(1) Demonstration of a reproducible R-based automated pipeline and train-of-thoughts from inception to deployment to streamline the collection, validation, summarisation, visualisation and reporting of recurrent global threat data.

(2) Demonstration of an AI-assisted solution on evidence synthesis to support transparent and more efficient assessment of impact, likelihood and overall risk of public health threats.

(3) Demonstration of unsupervised machine learning to identify meaningful subgroups within a fictional outbreak allowing tailored responses to subgroups within the outbreak.

(4) Demonstration of retrieval-augmented generation approach to produce evidence-based answers with traceable references to public health questions will be demonstrated.

(5) Hands-on activity to test and refine prompts or reusable skill files for scientific writing using their own draft manuscripts.

(6) Hands-on activity on using large language models for generating a REDCap project manual from an XML file , OR (participants choice)

(7) Hands-on activity on using a simulated Lassa fever exposure avatar to assess and improve their interactions with real potential imported Lassa fever cases.

Throughout, presenters will emphasise choosing the simplest fit-for-purpose approach, evaluating performance, recognising limitations and adapting solutions to participants' own settings.

Workshop hosted by (name, organisation): Laura Espinosa, European Centre for Disease Prevention and Control; Ben Scalley, WA Health; Craig Dalton, HNE Health