Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs. (23rd October 2022)

• Record Type: Journal Article
• Authors: Naumova, Elena N.; Simpson, Ryan B.; Zhou, Bingjie; Hartwick, Meghan A.
• Is Part Of: International statistical review. Volume 90(2022)Supplement 1
• Page Start: S82
• Page End: S95
• Publication Date: 2022-10-23
• Subjects: data quality -- influenza -- mixed effects models -- surveillance -- time series -- visualisation -- workflow optimization -- World Health Organization; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12529
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: 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.