Temporal analysis of respiratory virus epidemics in Victoria over winter 2024
DOI:
https://doi.org/10.33321/cdi.2026.50.015Keywords:
SARS-CoV-2, RSV, influenza, influenza subtypes, respiratory pathogensAbstract
During winter months of temperate regions, concurrent epidemics of multiple respiratory pathogens can occur, causing periods of increased clinical burden. Case time series, which are predominantly used to monitor infection levels, can exhibit substantial noise and day-of-the-week effects, limiting the visual interpretation of trends in raw data. However, statistical methods can infer smoothed trends within case time series by quantifying and accounting for different sources of noise. Here we apply statistical models to estimate the epidemic dynamics of SARS-CoV-2, respiratory syncytial virus (RSV), and influenza subtypes (influenza A H3N2, influenza A H1N1, and influenza B) in Victoria, Australia, over the 2024 winter season. We model trends in daily reported cases and the daily growth rate over time for all pathogens/subtypes. We present: (1) retrospective analyses using the final dataset up to 10 September 2024 and (2) weekly real-time analyses from 19 March 2024 to 10 September 2024 using data up to each timepoint, including a retrospective performance evaluation. We estimated similar peak timing of SARS-CoV-2 and RSV epidemics in late May, followed by a H3N2-dominant influenza epidemic, which peaked in early July. Transient increases in SARS-CoV-2 activity coincided with the emergence of new variants and transient decreases in influenza activity corresponded to the timing of school holidays. Real-time estimates demonstrated good agreement with those produced at the end of the season, with significant overlap of the 95% credible intervals. Our findings demonstrate how statistical methods can be implemented in real time to synthesise noisy case time-series data into interpretable trends (including uncertainty), enabling quantification of the strength of evidence for whether epidemic activity is increasing, stable or declining. Our real-time outputs were reported weekly to the Department of Health, Victoria during June–September 2024, complementing other routine surveillance indicators.
Downloads
References
Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG et al. Key challenges for respiratory virus surveillance while transitioning out of acute phase of COVID-19 pandemic. Emerg Infect Dis. 2024;30(2):e230768. doi: https://doi.org/10.3201/eid3002.230768.
Cohen J. Will viral interference hold off the tripledemic? Science. 2022;378(6622):814–5. doi: https://doi.org/10.1126/science.adf8989.
Parag KV, Thompson RN, Donnelly CA. Are epidemic growth rates more informative than reproduction numbers? J R Stat Soc Ser A Stat Soc. 2022:10.1111/rssa.12867. doi: https://doi.org/10.1111/rssa.12867.
Eales O, Ainslie KEC, Walters CE, Wang H, Atchison C, Ashby D et al. Appropriately smoothing prevalence data to inform estimates of growth rate and reproduction number. Epidemics. 2022;40:100604. doi: https://doi.org/10.1016/j.epidem.2022.100604.
Pei S, Shaman J. Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness. PLoS Comput Biol. 2020;16(10):e1008301. doi: https://doi.org/10.1371/journal.pcbi.1008301.
Eales O, McCaw JM, Shearer FM. Challenges in the case-based surveillance of infectious diseases. R Soc Open Sci. 2024;11(8):240202. doi: https://doi.org/10.1098/rsos.240202.
Eales O, McCaw JM, Shearer FM. Biases in routine influenza surveillance indicators used to monitor infection incidence and recommendations for improvement. Influenza Other Respi Viruses. 2024;18(12):e70050. doi: https://doi.org/10.1111/irv.70050.
Moss R, Zarebski AE, Carlson SJ, McCaw JM. Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts. Trop Med Infect Dis. 2019;4(1):12. doi: https://doi.org/10.3390/tropicalmed4010012.
Eales O, Teo M, Price DJ, Hao T, Ryan GE, Senior KL et al. Temporal trends in test-seeking behaviour during the COVID-19 pandemic. Math Med Life Sci. 2025;2(1). doi: https://doi.org/10.1080/29937574.2025.2521858.
Abbott S, Hellewell J, Thompson RN, Sherratt K, Gibbs HP, Bosse NI et al. Estimating the time-varying reproduction number of SARS-CoV-2 using national and subnational case counts. Wellcome Open Res. 2020;5:112. doi: https://doi.org/10.12688/wellcomeopenres.16006.2.
Eales O, Windecker SM, McCaw JM, Shearer FM. Inferring temporal trends of multiple pathogens, variants, subtypes or serotypes from routine surveillance data. Am J Epidemiol. 2025;kwaf119. doi: https://doi.org/10.1093/aje/kwaf119.
Baumeister E, Duque J, Varela T, Palekar R, Couto P, Savy V et al. Timing of respiratory syncytial virus and influenza epidemic activity in five regions of Argentina, 2007–2016. Influenza Other Respir Viruses. 2019;13(1):10–7. doi: https://doi.org/10.1111/irv.12596.
Reich NG, McGowan CJ, Yamana TK, Tushar A, Ray EL, Osthus D et al. Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. PLoS Comput Biol. 2019;15(11):e1007486. doi: https://doi.org/10.1371/journal.pcbi.1007486.
Pelat C, Lasserre A, Xavier A, Turbelin C, Blanchon T, Hanslik T. Hospitalization of influenza-like illness patients recommended by general practitioners in France between 1997 and 2010. Influenza Other Respir Viruses. 2013;7(1):74–84. doi: https://doi.org/10.1111/j.1750-2659.2012.00356.x.
Jeworowski LM, Mühlemann B, Walper F, Schmidt ML, Jansen J, Krumbholz A et al. Humoral immune escape by current SARS-CoV-2 variants BA.2.86 and JN.1, December 2023. Euro Surveill. 2024;29(2):2300740. doi: https://doi.org/10.2807/1560-7917.ES.2024.29.2.2300740.
Lewnard JA, Mahale P, Malden D, Hong V, Ackerson BK, Lewin BJ et al. Immune escape and attenuated severity associated with the SARS-CoV-2 BA.2.86/JN.1 lineage. Nat Commun. 2024;15(1):8550. doi: https://doi.org/10.1038/s41467-024-52668-w.
Gavenčiak T, Monrad JT, Leech G, Sharma M, Mindermann S, Bhatt S et al. Seasonal variation in SARS-CoV-2 transmission in temperate climates: a Bayesian modelling study in 143 European regions. PLoS Comput Biol. 2022;18(8):e1010435. doi: https://doi.org/10.1371/journal.pcbi.1010435.
covSPECTRUM. Detect and analyze variants of SARS-CoV-2: Australia 2024. [Website.] Zurich: Eidgenössische Technische Hochschule Zürich, cov-spectrum.org; 2021. [Accessed on 18 November 2024.] Available from: https://cov-spectrum.org/explore/Australia/AllSamples/Y2024.
Ewing A, Lee EC, Viboud C, Bansal S. Contact, travel, and transmission: the impact of winter holidays on influenza dynamics in the United States. J Infect Dis. 2017;215(5):732–9. doi: https://doi.org/10.1093/infdis/jiw642.
He C, Norton D, Temte JL, Barlow S, Goss M, Temte E et al. Effect of planned school breaks on student absenteeism due to influenza-like illness in school aged children—Oregon School District, Wisconsin September 2014–June 2019. Influenza Other Respir Viruses. 2024;18(1):e13244. doi: https://doi.org/10.1111/irv.13244.
Jackson C, Vynnycky E, Mangtani P. The relationship between school holidays and transmission of influenza in England and Wales. Am J Epidemiol. 2016;184(9):644–51. doi: https://doi.org/10.1093/aje/kww083.
Queensland State Government Department of Health (Queensland Health). Queensland acute respiratory infection surveillance report 1 January – 11 August 2024. Brisbane: Queensland Health; August 2024. [Accessed in August 2024.] Available from: https://www.health.qld.gov.au/clinical-practice/guidelines-procedures/diseases-infection/surveillance/reports/flu.
New South Wales State Government Department of Health (NSW Health). NSW Respiratory Surveillance Report – week ending 10 August 2024. Sydney: NSW Health; 10 August 2024. Available from: https://www.health.nsw.gov.au/Infectious/covid-19/Documents/respiratory-surveillance-20240727.pdf.
Hogan AB, Glass K, Moore HC, Anderssen RS. Exploring the dynamics of respiratory syncytial virus (RSV) transmission in children. Theor Popul Biol. 2016;110:78–85. doi: https://doi.org/10.1016/j.tpb.2016.04.003.
Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C et al. Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol. 2020;16(12):e1008409. doi: https://doi.org/10.1371/journal.pcbi.1008409.
Eales O, Riley S. Differences between the true reproduction number and the apparent reproduction number of an epidemic time series. Epidemics. 2024 Mar 1;46:100742. doi: https://doi.org/10.1371/journal.pcbi.1008409.
Burton T. NSW perilous as modelling suggests Victoria slowly flattening curve. [Online news article.] Sydney: Nine Entertainment, Australian Financial Review; 17 July 2020. [Accessed on 20 January 2025.] Available from: https://www.afr.com/politics/federal/nsw-perilous-as-modelling-suggests-victoria-slowly-flattening-curve-20200717-p55cz7.
Moss R, Price DJ, Golding N, Dawson P, McVernon J, Hyndman RJ et al. Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020. Sci Rep. 2023;13(1):8763. doi: https://doi.org/10.1038/s41598-023-35668-6.
R Core Team. R: A Language and Environment for Statistical Computing. [Application.] Vienna: R Foundation for Statistical Computing; 2023. Available from: https://www.R-project.org/.
Guo J, Gabry J, Goodrich B, Johnson A, Weber S, Stan Development Team. RStan: the R interface to Stan. [Application.] 2020. Available from: https://mc-stan.org/rstan/.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. 2nd ed. New York: Springer-Verlag, 2016. Available from: https://ggplot2-book.org/.
Pedersen TL. patchwork: The Composer of Plots. [Software.] 2024. Available from: https://patchwork.data-imaginist.com.
Neuwirth E. RColorBrewer: ColorBrewer Palettes. 2022. Available from: https://CRAN.R-project.org/package=RColorBrewer.
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Communicable Diseases Intelligence
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
