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Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs

Author

Listed:
  • Elena N. Naumova
  • Ryan B. Simpson
  • Bingjie Zhou
  • Meghan A. Hartwick

Abstract

The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate the seasonal characteristics and their uncertainty using mixed effects models with harmonic components and the δ‐method and develop multi‐panel visualisations to present complex interplay of seasonal peaks across geographic locations. We compiled a set of 2 422 weekly time series of 14 reported outcomes for 173 Member States from the World Health Organization's (WHO) international influenza virological surveillance system, FluNet, from 02 January 1995 through 20 June 2021. We produced an analecta of data visualisations to describe global travelling waves of influenza while addressing issues of data completeness and credibility. Our results offer directions for further improvements in data collection, reporting, analysis and development of statistical methodology and predictive approaches.

Suggested Citation

  • Elena N. Naumova & Ryan B. Simpson & Bingjie Zhou & Meghan A. Hartwick, 2022. "Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 82-95, December.
    • Handle: RePEc:bla:istatr:v:90:y:2022:i:s1:p:s82-s95
    DOI: 10.1111/insr.12529
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    References listed on IDEAS

    as
    1. Katarina Ureña-Castro & Silvia Ávila & Mariela Gutierrez & Elena N. Naumova & Rolando Ulloa-Gutierrez & Alfredo Mora-Guevara, 2019. "Seasonality of Rotavirus Hospitalizations at Costa Rica’s National Children’s Hospital in 2010–2015," IJERPH, MDPI, vol. 16(13), pages 1-13, June.
    2. Ryan B. Simpson & Brianna N. Lauren & Kees H. Schipper & James C. McCann & Maia C. Tarnas & Elena N. Naumova, 2022. "Critical Periods, Critical Time Points and Day-of-the-Week Effects in COVID-19 Surveillance Data: An Example in Middlesex County, Massachusetts, USA," IJERPH, MDPI, vol. 19(3), pages 1-20, January.
    3. Tania M. Alarcon Falconi & Bertha Estrella & Fernando Sempértegui & Elena N. Naumova, 2020. "Effects of Data Aggregation on Time Series Analysis of Seasonal Infections," IJERPH, MDPI, vol. 17(16), pages 1-21, August.
    4. Pavel S. Stashevsky & Irina N. Yakovina & Tania M. Alarcon Falconi & Elena N. Naumova, 2019. "Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    5. Nan M. Laird, 2022. "Statistical analysis of longitudinal studies," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 2-16, December.
    6. Julia B Wenger & Elena N Naumova, 2010. "Seasonal Synchronization of Influenza in the United States Older Adult Population," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-11, April.
    Full references (including those not matched with items on IDEAS)

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