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Exploring different modelling approaches to forecast the community acute respiratory infections burden in children:

Riccardo Boracchini1,2, Benedetta Canova3, Pietro Ferrara4,5

  • 1Department of Statistics and Quantitative Methods, Division of Biostatistics, Epidemiology and Public Health, Laboratory of Healthcare Research and Pharmacoepidemiology, University of Milan-Bicocca, Via Bicocca Degli Arcimboldi, 8, Milan, 20126, Italy. riccardo.boracchini@unimib.it.

BMC Public Health
|February 28, 2025
PubMed

Insights

Exponential smoothing models like ETS and TBATS are most effective for forecasting pediatric acute respiratory infections (ARIs) burden in Italy. Continued monitoring is needed to assess lingering pandemic effects on respiratory illness patterns.

Area of Science:

  • Epidemiology
  • Time Series Analysis
  • Pediatric Health

Background:

  • Acute respiratory infections (ARIs) are a major cause of mortality in young children globally.
  • Italy faces a significant burden of pediatric ARIs, necessitating accurate forecasting.
  • Understanding seasonal trends is crucial for public health interventions.

Purpose of the Study:

  • To identify the most effective time series models for forecasting the 2023/2024 ARI season burden in Italian children.
  • To compare the predictive accuracy of various statistical models for pediatric ARIs.
  • To provide data-driven insights for managing ARI outbreaks.

Main Methods:

  • Utilized monthly ARI incidence data (2010-2023) from the Pedianet database (children 0-14 years).
  • Compared Error, Trend, Seasonality (ETS), Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Unobserved Component Model (UCM), and Trigonometric, Box Cox, ARMA errors, Trend, Seasonal (TBATS) models.
  • Evaluated model accuracy using residuals and Mean Absolute Percentage Error (MAPE), with a COVID-19 period adjustment.

Main Results:

  • Exponential smoothing models, specifically ETS (MAPE=6.85) and TBATS (MAPE=6.87), demonstrated superior accuracy in forecasting ARI burden.
  • UCM (MAPE=11.08) and SARIMA (MAPE=25.33) models showed significantly lower predictive performance.
  • Over 1.4 million ARI cases in children aged 0-14 years were analyzed.

Conclusions:

  • Exponential smoothing models (ETS, TBATS) are recommended for forecasting pediatric ARI trends in Italy.
  • Ongoing surveillance is essential to determine if residual pandemic effects influence current respiratory infection patterns.
  • Accurate forecasting aids in resource allocation and public health preparedness for pediatric respiratory illnesses.
Abstract

Keywords:
Acute Respiratory InfectionsBurdenTime series models comparison

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