This research evaluates electronic case reporting (ECR), an automated disease surveillance system that alerts public health agencies as soon as diagnoses are recorded. By analyzing surveillance data and clinician experiences, the work aims to improve outbreak detection speed, accuracy, and usability—helping public health respond earlier and save lives.

This research develops a computational method for detecting hidden RNA viruses within existing RNA sequencing datasets. By identifying conserved viral protein signatures, the approach enables large-scale discovery of previously unknown viruses, improving understanding of viral diversity, disease mechanisms, and future opportunities for diagnostics, surveillance, and antiviral treatment development.