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BMC Public Health. 2019 Sep 5;19(1):1232. doi: 10.1186/s12889-019-7521-7.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.

Author information

1
Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada. mward06@uoguelph.ca.
2
Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.
3
Department of Production Animal Health, University of Calgary, University Drive NW, Calgary, T2N 1N4, Canada.
4
Department of Mathematics and Statistics, University of Calgary, University Drive NW, Calgary, T2N 1N4, Canada.
5
Wellington-Dufferin-Guelph Public Health, Chancellors Way, Guelph, N1G 0E1, Canada.

Abstract

BACKGROUND:

School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics.

METHODS:

Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness.

RESULTS:

All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827.

CONCLUSIONS:

School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

KEYWORDS:

Absenteeism surveillance system; Disease modelling; Epidemic detection; Influenza; Seasonal logistic regression

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