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 generat

SUBMITTER: Lim JT 

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

REPOSITORIES: biostudies-literature