Peer-reviewedCC BY-NCInfluenza and other respiratory viruses1 Nov 2026

Validating Time‐Series Regression Model Predictions of Influenza Mortality: An Interrupted Time‐Series Analysis

Jessie J. Goldsmith^1,2
David J. Price^1,3
Katherine B. Gibney^1,4,5
Patricia Therese Campbell¹
Sheena G. Sullivan⁴

5 affiliations

¹Department of Infectious Diseases University of Melbourne at the Peter Doherty Institute for Infection and Immunity Melbourne Australia
²WHO Collaborating Centre for Reference and Research on Influenza Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity Melbourne Australia
³Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health University of Melbourne Melbourne Australia
⁴School of Clinical Sciences Monash University Clayton Australia
⁵Monash Infectious Diseases Monash Health Clayton Australia

Abstract

Background: In Australia, non‐pharmaceutical interventions introduced early in 2020 to curtail the spread of COVID‐19 resulted in the temporary elimination of influenza. We used data from this influenza‐free period (2021) to validate influenza mortality burden predictions from time‐series models.

Methods: Time‐series models were parameterised with routinely collected health data from 2010 to 2019 (pre‐pandemic) and from 2010 to 2019 plus 2021 (pandemic‐inclusive). These models were used to predict influenza A‐ and influenza B‐associated mortality rates in Queensland between 2010 and 2019. Mortality estimates were made in persons < 65 and ≥ 65 years for pneumonia and influenza (P&I) and respiratory illnesses as contributory mortality causes and for all‐cause mortality.

Results: The models predicted consistent mortality rates for influenza B. However, the pandemic‐inclusive model for individuals ≥ 65 years predicted influenza A‐associated P&I mortality of 32.1 per 100,000 person‐years (95% prediction interval [PI]: 25.9, 39.6), significantly higher than the pre‐pandemic model estimate of 17.6 (95% PI: 13.4, 23.6). A similar, non‐significant difference occurred for respiratory mortality.

Conclusions: These results suggest that time‐series modelling may underestimate the true burden of influenza‐A mortality among the elderly. Further studies are needed to confirm this finding and establish the conditions under which the time‐series regression approach may be reliable.

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Cite

BibTeX RIS

BibTeX

@article{goldsmith2026validating,
  title = {{Validating Time‐Series Regression Model Predictions of Influenza Mortality: An Interrupted Time‐Series Analysis}},
  author = {Jessie J. Goldsmith and David J. Price and Katherine B. Gibney and Patricia Therese Campbell and Sheena G. Sullivan},
  journal = {Influenza and other respiratory viruses},
  year = {2026},
  month = nov,
  doi = {10.1111/irv.70340},
  url = {https://europepmc.org/article/PMC/PMC13730788},
  note = {CC BY-NC},
}

RIS

TY  - JOUR
TI  - Validating Time‐Series Regression Model Predictions of Influenza Mortality: An Interrupted Time‐Series Analysis
AU  - Jessie J. Goldsmith
AU  - David J. Price
AU  - Katherine B. Gibney
AU  - Patricia Therese Campbell
AU  - Sheena G. Sullivan
PY  - 2026
DA  - 2026/11/01
JO  - Influenza and other respiratory viruses
DO  - 10.1111/irv.70340
UR  - https://europepmc.org/article/PMC/PMC13730788
AB  - Background: In Australia, non‐pharmaceutical interventions introduced early in 2020 to curtail the spread of COVID‐19 resulted in the temporary elimination of influenza. We used data from this influenza‐free period (2021) to validate influenza mortality burden predictions from time‐series models.  Methods: Time‐series models were parameterised with routinely collected health data from 2010 to 2019 (pre‐pandemic) and from 2010 to 2019 plus 2021 (pandemic‐inclusive). These models were used to predict influenza A‐ and influenza B‐associated mortality rates in Queensland between 2010 and 2019. Mortality estimates were made in persons < 65 and ≥ 65 years for pneumonia and influenza (P&I) and respiratory illnesses as contributory mortality causes and for all‐cause mortality.  Results: The models predicted consistent mortality rates for influenza B. However, the pandemic‐inclusive model for individuals ≥ 65 years predicted influenza A‐associated P&I mortality of 32.1 per 100,000 person‐years (95% prediction interval [PI]: 25.9, 39.6), significantly higher than the pre‐pandemic model estimate of 17.6 (95% PI: 13.4, 23.6). A similar, non‐significant difference occurred for respiratory mortality.  Conclusions: These results suggest that time‐series modelling may underestimate the true burden of influenza‐A mortality among the elderly. Further studies are needed to confirm this finding and establish the conditions under which the time‐series regression approach may be reliable.
SN  - 1750-2640
SN  - 1750-2659
ER  -

Abstract

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