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An adaptive method of emergency department syndromic surveillance to nowcast the frequency of presentations that will have a severe 28-day outcome following influenza or COVID-19 infection: a retrospective analytical record linkage study

David J MuscatelloUNSW Sydney
Nectarios RoseUNSW Sydney
Kishor Kumar PaulUNSW Sydney
Alexandra B. HoganUNSW Sydney
Amalie DydaQueensland Health
Michael DinhRoyal Prince Alfred Hospital
Jen KokWestmead Hospital
Sandra WareNSW Ambulance
Adam CraigThe University of Queensland
James WoodUNSW Sydney
American Journal of Epidemiology·February 4, 2026
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Abstract

During epidemics, emergency department (ED) syndromic surveillance of patient arrivals provides timely but non-virus-specific assessment of epidemic intensity. Surveillance of severe infection outcomes (intensive care admission or death) is less timely because outcomes can take weeks to occur. Time series models can be used to estimate the frequency of severe infection outcomes due to viruses. We developed and evaluated daily time series modelling applied to linked ED, infection and outcomes data from Australia to better predict population and health system burden during acute respiratory virus epidemics. In retrospective daily surveillance emulation, generalised additive models nowcasted (short-term forecast) the frequency of ED arrivals attributable to each of influenza and COVID-19 that will have a severe infection outcome within 28 days. Daily nowcasts spanned days -29 to -4 from each date for which surveillance was emulated. To validate the method, nowcasts were compared with subsequently observed severe infection outcome frequencies for December 2021 through February 2023. During this period, the mean daily day -4 nowcast error was 2.7 (34.2%), compared with 3.5 (43.8%) if outcomes known at day -1 were used. With increasing real-world data availability, this method could improve rapid, automated epidemic assessment for timely public health action.

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