Magda Bucholc (Ulster University) – Implementation of the Farrington method for outbreak detection on routine surveillance data

Effective infectious disease surveillance is essential to initiating control measures to protect the public’s health in a timely manner. While traditional surveillance methods remain crucial, supplementing them with automated approaches, such as surveillance algorithms, can improve the system’s ability to detect potential outbreaks for investigation and control. In this project, we focused on the implementation of the multi-purpose outbreak detection algorithm to enhance surveillance capabilities of the PHA Health Protection Surveillance team. Implemented in R, the improved version of the Farrington algorithm has been integrated into an Infectious Signal Detection tool, an interactive Shiny application, facilitating user-friendly exploration of time series for signal detection. Our tool allows users to import external data or use direct data pipelines and analyse such data through a set of choices of parameters.