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

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Abstract
During epidemics, emergency department (ED) syndromic surveillance of patient arrivals provides timely but nonvirus-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 modeling 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, generalized 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.
Funding Information
  • Australian National Health and Medical Research Council (APP2009278)
  • Australian National Health and Medical Research Council (APP1194109)

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