Dataset Information

Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.

ABSTRACT: Upper respiratory tract infections (URTIs) represent a large strain on primary health resources. To mitigate URTI transmission and public health burdens, it is important to pre-empt and provide forward guidance on URTI burden, while taking into account various facets which influence URTI transmission. This is so that appropriate public health measures can be taken to mitigate strain on primary care resources. This study describes a new approach to forecasting URTIs which can be used for national public health resource planning. Specifically, using environmental and disease data comprising more than 1000 dimensions, we developed sub-models which optimizes model explainability, in-sample model fit, predictive accuracy and combines many weaker predictors over a 2-month time horizon to generate direct, point forecasts over a 1-8 week ahead forecast horizon. Predictive performance was evaluated using rolling out-of-sample forecast assessment within both periods with/without structural breaks in transmission over the period of 2012-2022. We showed that forecast combinations of 5 other forecasting models had better and more consistent predictive performance than other modelling approaches, over periods with and without structural breaks in transmission dynamics. Furthermore, epidemiological analysis on high dimensional data was enabled using post-selection inference, to show the dynamic association between lower temperature, increases in past relative humidity and absolute humidity and increased URTIs attendance. The methods proposed can be used for outbreak preparedness and guide healthcare resource planning, in both stable periods of transmission and periods where structural breaks in data occur.

SUBMITTER: Lim JT

PROVIDER: S-EPMC9983836 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.

Lim Jue Tao JT Tan Kelvin Bryan KB Abisheganaden John J Dickens Borame L BL

PLoS computational biology 20230207 2

Upper respiratory tract infections (URTIs) represent a large strain on primary health resources. To mitigate URTI transmission and public health burdens, it is important to pre-empt and provide forward guidance on URTI burden, while taking into account various facets which influence URTI transmission. This is so that appropriate public health measures can be taken to mitigate strain on primary care resources. This study describes a new approach to forecasting URTIs which can be used for national ...[more]

PMID: 36749792

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Project description:BackgroundAcute respiratory infections (ARIs) in young children pose a significant global health challenge, leading to high rates of illness and death. They are estimated to be the fourth leading cause of mortality worldwide, particularly impacting children under five. This study aimed to identify the most effective time series model(s) for forecasting the epidemiological season burden of ARIs for the current 2023/2024 period in Italy.MethodsData on the burden of ARIs' in children aged 0-14 years were retrieved from Pedianet, an Italian paediatric primary care database which includes over 200 family paediatricians. We analysed monthly incidence rates of ARIs from September 2010 to September 2023, following the typical seasonal pattern of these infections. Several forecasting models were compared to predict the future burden of ARI: Error, Trend, Seasonality (ETS); Seasonal Auto-Regressive Integrated Moving Average (SARIMA); Unobserved Component Model (UCM); and Trigonometric, Box Cox, ARMA errors, Trend, Seasonal (TBATS). We evaluated each model's accuracy by examining the residuals and the Mean Absolute Percentage Error (MAPE). The period between March 2020 and February 2022 was forecasted to represent the normal trend without COVID-19. Model parameters were estimated using the in-sample and out-of-sample approach.ResultsThe analysis included data from over 1.4 million cases of ARIs retrieved in children aged 0-14 years. The ETS model was implemented to predict the pandemic period. Overall, our findings suggest that exponential smoothing models as ETS (MAPE = 6.85) and TBATS (MAPE = 6.87) were most effective in predicting future trends in monthly ARIs' burden compared to other methods (i.e., UCM MAPE = 11.08, and SARIMA MAPE = 25.33).ConclusionsThese findings suggest that exponential smoothing models are preferable for forecasting pediatric ARIs' burden trends in Italy. However, epidemiological data from the ongoing season are crucial for understanding whether residual pandemic effects continue affecting respiratory infection patterns.

Dataset Information

Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.

Publications

Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations.

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