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

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 transmissio...

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Autores principales: Lim, Jue Tao, Tan, Kelvin Bryan, Abisheganaden, John, Dickens, Borame L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983836/
https://www.ncbi.nlm.nih.gov/pubmed/36749792
http://dx.doi.org/10.1371/journal.pcbi.1010892
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author Lim, Jue Tao
Tan, Kelvin Bryan
Abisheganaden, John
Dickens, Borame L.
author_facet Lim, Jue Tao
Tan, Kelvin Bryan
Abisheganaden, John
Dickens, Borame L.
author_sort Lim, Jue Tao
collection PubMed
description 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.
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spelling pubmed-99838362023-03-04 Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations Lim, Jue Tao Tan, Kelvin Bryan Abisheganaden, John Dickens, Borame L. PLoS Comput Biol Research Article 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. Public Library of Science 2023-02-07 /pmc/articles/PMC9983836/ /pubmed/36749792 http://dx.doi.org/10.1371/journal.pcbi.1010892 Text en © 2023 Lim et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lim, Jue Tao
Tan, Kelvin Bryan
Abisheganaden, John
Dickens, Borame L.
Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations
title Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations
title_full Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations
title_fullStr Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations
title_full_unstemmed Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations
title_short Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations
title_sort forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983836/
https://www.ncbi.nlm.nih.gov/pubmed/36749792
http://dx.doi.org/10.1371/journal.pcbi.1010892
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