loading page

Predicting virologically-confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons
  • +9
  • Talia Quandelacy,
  • Shanta Zimmer,
  • Justin Lessler,
  • Charles VUKOTICH,
  • Rachel Bieltz,
  • Kyra Grantz,
  • David Galloway,
  • Jonathan Read,
  • Yenlik Zheteyeva,
  • Hongjiang Gao,
  • Amra Uzicanin,
  • Derek Cummings
Talia Quandelacy
Johns Hopkins University Bloomberg School of Public Health
Author Profile
Shanta Zimmer
University of Pittsburgh School of Medicine
Author Profile
Justin Lessler
Johns Hopkins Bloomberg School of Public Health
Author Profile
Charles VUKOTICH
University of Pittsburgh
Author Profile
Rachel Bieltz
University of Pittsburgh
Author Profile
Kyra Grantz
University of Florida
Author Profile
David Galloway
University of Pittsburgh
Author Profile
Jonathan Read
University of Liverpool
Author Profile
Yenlik Zheteyeva
Centers for Disease Control and Prevention
Author Profile
Hongjiang Gao
Centers for Disease Control and Prevention
Author Profile
Amra Uzicanin
Author Profile
Derek Cummings
University of Florida
Author Profile

Abstract

Background Children are important in community-level influenza transmission. School-based monitoring may inform influenza surveillance. Methods We used reported weekly confirmed influenza in Allegheny County during the 2007, and 2010-2015 influenza seasons using Pennsylvania’s Allegheny County Health Department all-age influenza cases from health facilities, and all-cause and influenza-like illness (ILI)-specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all-cause and illness-specific absence rates, calendar week, average weekly temperature and relative humidity, using four cross-validations. Results School districts reported 2,184,220 all-cause absences (2010-2015). Three one-season studies reported 19,577 all-cause and 3,012 ILI-related absences (2007, 2012, 2015). Over seven seasons, 11,946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE)=0.94, 0.98, 0.99). K-5 grade-specific absence models had lowest mean absolute errors (MAE) in cross-validations. ILI-specific absences performed marginally better than all-cause absences in two years, adjusting for other covariates, but markedly worse one year. Conclusions Our findings suggest seasonal models including K-5th grade absences predict all-age confirmed influenza and may serve as a useful surveillance tool.

Peer review status:ACCEPTED

22 Jan 2021Submitted to Influenza and other respiratory viruses
23 Jan 2021Assigned to Editor
23 Jan 2021Submission Checks Completed
14 Feb 2021Editorial Decision: Revise Minor
30 Mar 20211st Revision Received
31 Mar 2021Submission Checks Completed
31 Mar 2021Assigned to Editor
31 Mar 2021Editorial Decision: Accept