Journal Article

Forecasting severe respiratory disease hospitalizations using machine learning algorithms

Castelino, Lorraine, Dost, Katharina, Wu, Milton, Zhu, Johnny, Riddle, Patricia, McIntyre, Peter, Broderick, David, Marsh, Samantha, Paynter, Janine, Nghiem, Nhung, Wicker, Jörg Simon, Dobbie, Gillian, Huang, Sue, Poonawala-Lohani, Nooriyan, Jamison, Sarah, Turner, Nikki, Albrecht, Steffen, Stanley, Alicia, Lawrence, Shirley, Trenholme, Adrian, Grant, Cameron, Rasanathan, Damayanthi, Cheung, Isabella

2024

Overview

Background Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload. In this study, we explore the capability of forecasting models to predict the number of hospital admissions in Auckland, New Zealand, within a three-week time horizon. Furthermore, we evaluate probabilistic forecasts and the impact on model performance when integrating laboratory data describing the circulation of respiratory viruses. Methods The dataset used for this exploration results from active hospital surveillance, in which the World Health Organization Severe Acute Respiratory Infection (SARI) case definition was consistently used. This research nurse-led surveillance has been implemented in two public hospitals in Auckland and provides a systematic laboratory testing of SARI patients for nine respiratory viruses, including influenza, respiratory syncytial virus, and rhinovirus. The forecasting strategies used comprise automatic machine learning, one of the most recent generative pre-trained transformers, and established artificial neural network algorithms capable of univariate and multivariate forecasting. Results We found that machine learning models compute more accurate forecasts in comparison to naïve seasonal models. Furthermore, we analyzed the impact of reducing the temporal resolution of forecasts, which decreased the model error of point forecasts and made probabilistic forecasting more reliable. An additional analysis that used the laboratory data revealed strong season-to-season variations in the incidence of respiratory viruses and how this correlates with total hospitalization cases. These variations could explain why it was not possible to improve forecasts by integrating this data. Conclusions Active SARI surveillance and consistent data collection over time enable these data to be used to predict hospital bed utilization. These findings show the potential of machine learning as support for informing systems for proactive hospital management.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Age groups / Algorithms / Artificial neural networks / Computational tools for infection control / COVID-19 / Data analysis / Data collection / Datasets / Disease management / Disease transmission / Error analysis / Flu prediction / Forecasting / Forecasting healthcare burden / Forecasts and trends / Health Informatics / Hospital care / Hospitalization - statistics & numerical data / Hospitals / Humans / Illnesses / Impact analysis / Infections / Influenza / Influenza-like illness / Information Systems and Communication Service / Laboratories / Laboratory tests / Learning algorithms / Machine Learning / Management of Computing and Information Systems / Medicine / Medicine & Public Health / Methods / Neural networks / Neural Networks, Computer / New Zealand - epidemiology / Pandemics / Patient admissions / Respiratory diseases / Respiratory syncytial virus / Respiratory tract diseases / Respiratory tract infection / Respiratory Tract Infections - epidemiology / Rhinovirus / Risk factors / Seasonal epidemic / Sentinel health events / Severe respiratory diseases / Social distancing / Software / Statistical methods / Statistics / Surveillance / Temporal resolution / Time series / Viruses