Development of the Expected Severity Divergence Score (ESDS) to Enable Targeted Characterization and Surveillance of Influenza: A Retrospective Cohort Analysis (Preprint)

作者：Chase Yonamine, Gaby Dashler, Xihan Zhao, Martin Copenhaver, Fenstermacher Katherine, Richard Rothman, Jeremiah Hinson, Eili Klein · 年份：2026 · DOI：10.2196/preprints.105463 · 研究领域：Data-Driven Disease Surveillance、Influenza Virus Research Studies、Respiratory viral infections research

BACKGROUND Antigenic immune escape allows seasonal influenza to persist as a global public health threat, with ongoing risk for zoonotic spillover and emergence of novel strains with pandemic potential. Emergency departments (EDs), which serve a diverse demographic of patients and regularly evaluate patients with respiratory illness, are frequently utilized for sentinel surveillance. However, existing ED-based surveillance systems are limited by the ability to identify patients whose illness severity deviates from expected patterns. OBJECTIVE To develop and evaluate the Expected Severity Divergence Score (ESDS), a patient-level metric designed to identify individuals whose observed clinical severity differs from model-predicted expectations, with potential application to targeted influenza surveillance. METHODS We conducted a retrospective cohort study across five EDs within a large academic health system (August 2018–August 2024). Adult patients with laboratory-confirmed influenza were included. Clinical, demographic, prior utilization, and medication data were extracted from the electronic health record. Two logistic regression models were developed to estimate the probability of hospital admission to either an inpatient ward or an intermediate/intensive care unit (IMC/ICU). ESDS measured the discordance between predicted admission probability and observed outcome, with higher values indicating greater divergence between expected and observed severity. Model pe...