Validating Time‐Series Regression Model Predictions of Influenza Mortality: An Interrupted Time‐Series Analysis
Year: 2026
Type: article
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.
DOI: 10.1111/irv.70340
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,840
OpenAlex ID: W7225155877