Influenza and other respiratory viruses · 2026
Applying the Moving Epidemic Method to Emergency Department Syndromic Surveillance to identify the Start of Influenza Seasons in New South Wales, Australia.
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Rose Nectarios, Craig Adam T, Muscatello David J
Аннотация
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.
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.