Abstract
For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for reverse transcription polymerase chain reaction (RT-PCR) influenza tests. In this study, we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.
Highlights
Public health surveillance systems provide valuable insights into disease incidence, which can inform preparedness and response activities [1]
Retrospective analysis revealed that in the 2017 influenza season there was a marked increase in the probability that Flutracking participants with influenza-like illnesses (ILI) symptoms would seek healthcare and have a specimen collected for testing, relative to previous influenza seasons, in all states except Western Australia
Incorporating the behavioural trends estimated from the Flutracking self-reported influenza tests into the 2014–2016 forecasts greatly improved influenza forecast performance in both Brisbane and Melbourne, but had negligible effect on performance in Sydney and substantially reduced performance in Perth
Summary
Public health surveillance systems provide valuable insights into disease incidence, which can inform preparedness and response activities [1]. The importance of understanding how people are identified by a surveillance system is problematic in the context of near-real-time influenza forecasting [2,3,4,5,6,7,8,9,10], because healthcare-seeking behaviours and clinical decision-making are dynamic [11,12] and are subject to acute influences (e.g., media coverage [13]) The challenge that this poses is further compounded by delays in data collection and reporting, which reduce forecast performance [9,14,15].
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