Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007‐2015 influenza seasons. Issue 6 (3rd September 2021)

• Record Type: Journal Article
• Authors: Quandelacy, Talia M.; Zimmer, Shanta; Lessler, Justin; Vukotich, Charles; Bieltz, Rachel; Grantz, Kyra H.; Galloway, David; Read, Jonathan M.; Zheteyeva, Yenlik; Gao, Hongjiang; Uzicanin, Amra; Cummings, Derek A. T.
• Is Part Of: Influenza and other respiratory viruses. Volume 15:Issue 6(2021)
• Page Start: 757
• Page End: 766
• Publication Date: 2021-09-03
• Subjects: human influenza -- prediction -- school‐aged children -- surveillance; Influenza -- Periodicals; Respiratory infections -- Periodicals; Virus diseases -- Periodicals; Influenza, Human -- Periodicals; Respiratory Tract Diseases -- Periodicals; Virus Diseases -- Periodicals; Grippe -- Périodiques; Appareil respiratoire -- Infections -- Périodiques; Maladies à virus -- Périodiques; 616.203
• DOI: 10.1111/irv.12865
• Languages: English
• ISSNs: 1750-2640

Abstract

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 3012 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.