Nectarios Rose

Background: The Moving Epidemic Method (MEM) is a widely used threshold‐based approach for identifying the start of an influenza season but has rarely been used with emergency department (ED) syndromic surveillance (EDSyS). This retrospective study aimed to evaluate MEM applied to rates of ED presentation for conditions compatible with influenza to calculate thresholds for identifying the start of the influenza season (pre‐epidemic thresholds) in New South Wales, Australia, 2012 through 2019. Methods: MEM was tested on weekly rates of ED presentations for three Syndromic Groups based on combinations of four diagnostic syndromes related to influenza, to calculate pre‐epidemic thresholds (epidemic start threshold) for the years 2012–2019. Validation included comparing the start of a season using pre‐epidemic thresholds with the start of the season based on percent positive influenza notifications from sentinel laboratories (identification difference). Results: The most accurate model for identifying the start of the influenza season combined all four diagnostic syndromes, consisting of influenza like illness, unspecified viral illness, pneumonia and lower respiratory tract infections. Pre‐epidemic thresholds based on MEM were able to identify the influenza season from between 2 weeks before to 1 week after the start, with a mean of 0.6 weeks before the start of a season and a mean absolute identification difference of 1.1 weeks. Conclusion: This study demonstrates the utility of MEM with EDSyS for identifying the start of influenza seasons, providing a visual alert for optimal timing of public health preparedness and response.

Emergency department (ED) syndromic surveillance (EDSyS) often relies on preliminary or ED discharge diagnosis codes as indicators of influenza, but few studies provide a justification for their selection. This retrospective analytical study aimed to optimise the selection of diagnostic codes in EDSyS for monitoring influenza activity and severity. Diagnostic codes potentially relating to a respiratory infection and assigned to people presenting to over 180 EDs in New South Wales (NSW), Australia, were grouped into 16 mutually exclusive 'ED Syndromes'. Time series of the proportion of ED presentations for each ED syndrome by epidemiological week between 2010 and 2019 were compared to a reference series of the percentage influenza positive results from sentinel laboratories, using two similarity and three timeliness statistics. Hospital inpatient admission and laboratory notification data linked to each ED presentation allowed assessment of patient infection status and outcomes. 'Unspecified Viral' (any non-specific viral illness, without reference to the respiratory system) and ED syndromes based on influenza like Illness (ILI) and influenza had the best combination of similarity and timeliness measures. Linked data identified relatively high rates of hospital admission, laboratory-confirmed influenza and inpatient influenza diagnosis for ED syndromes based on pneumonia and lower respiratory tract infection. In addition to ILI and influenza, ED syndromes based on unspecified viral illnesses can be used for EDSyS to assess influenza timing and transmissibility in NSW, Australia. The approach outlined in our paper can identify diagnostic codes to improve severity assessment of seasonal influenza using EDSyS.

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.

Geographic information systems (GIS) have emerged in the past few decades as a technology capable of assisting in the control of infectious disease outbreaks. A Legionnaires’ disease cluster investigation in May 2016 in Sydney, New South Wales (NSW), Australia, demonstrated the importance of using GIS to identify at-risk water sources in real-time for field investigation to help control any immediate environmental health risk, as well as the need for more staff trained in the use of this technology. Sydney Local Health District Public Health Unit (PHU) subsequently ran an exercise (based on this investigation) with 11 staff members from 4 PHUs across Sydney to further test staff capability to use GIS across NSW. At least 80% of exercise participants reported that the scenario progression was realistic, assigned tasks were clear, and sufficient data were provided to complete tasks. The exercise highlighted the multitude of geocoding applications and need for inter-operability of systems, as well as the need for trained staff with specific expertise in spatial analysis to help assist in outbreak control activity across NSW. Evaluation data demonstrated the need for a common GIS, regular education and training, and guidelines to support the collaborative use of GIS for infectious disease epidemiology in NSW. (Disaster Med Public Health Preparedness. 2019;13:372–374)