Influenza epidemiology and co-infections within New South Wales-based multicentre health districts between 2018 and 2023
Bibliographic record
Abstract
Influenza epidemiology spanning pre-COVID-19 pandemic to post-COVID-19 pandemic periods in Australia is insufficiently described. This study reviewed influenza epidemiology in two metropolitan New South Wales (NSW) health districts between 2018 and 2023 and investigated influenza virus (IFV) co-infections with other respiratory viruses (ORVs). A retrospective analysis of diagnostic polymerase chain reaction data from patients requiring testing for IFV and/or ORVs was conducted. Influenza detections were exceptionally low (n=57, <0.2% positivity) between April 2020 and 2021 when compared to those in 2019 (n=3,312, 14.4% positivity). Subsequent relaxation of public health measures corresponded with increased positivity rates: from 0.1% (33) in 2021 to 2.1% (4,028) in 2022 and 3.8% (4,362) in 2023. Influenza A virus (IAV) activity peaked earlier in 2022 and 2023 compared to most prepandemic years. Influenza B virus (IBV) detections were notably higher in 2019 and 2023. Co-infections were identified in 17.2% (346/2010) of IFV-positive samples, with rhinovirus being the most frequent co-infecting virus (7.4%). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was only detected in 1.3% of IFV infections. Logistic regression revealed significantly higher odds of IFV co-infections in children aged under 5 years [odds ratio (OR) 8.18; 95% confidence interval (CI) 5.44-12.29; p<0.01] and in those aged 5-17 years (OR 2.45; 95% CI 1.59-3.77; p<0.01). A significant increase in the likelihood of IFV co-infection was also observed in 2022 (OR 2.42; 95% CI 1.23-4.75; p<0.05). This study described influenza epidemiology across pre-COVID-19 pandemic, during-COVID-19 pandemic, and post-COVID-19 pandemic periods in NSW. Key findings include the earlier IAV peak activity in 2022-2023 and a rapid increase in IBV detection rate from 2022 to 2023, underscoring the need for sustained influenza surveillance to monitor the persistence of these trends. The surge in influenza detections in 2022-2023, accompanied with increased testing volumes, suggests that future surveillance efforts should account for changes in rates of testing when assessing severity of influenza seasons. The higher IFV co-infection frequency was observed in children and adolescents. 'Flurona' cases remain infrequent and exclusively associated with IAVs. These insights also inform the future application of multiplex diagnostic methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".