Real-time COVID-19 hospital admissions forecasting with leading indicators and ensemble methods in England

by   Jonathon Mellor, et al.

Hospitalisations from COVID-19 with Omicron sub-lineages have put a sustained pressure on the English healthcare system. Understanding the expected healthcare demand enables more effective and timely planning from public health. We collect syndromic surveillance sources, which include online search data, NHS 111 telephonic and online triages. Incorporating this data we explore generalised additive models, generalised linear mixed-models, penalised generalised linear models and model ensemble methods to forecast over a two-week forecast horizon at an NHS Trust level. Furthermore, we showcase how model combinations improve forecast scoring through a mean ensemble, weighted ensemble, and ensemble by regression. Validated over multiple Omicron waves, at different spatial scales, we show that leading indicators can improve performance of forecasting models, particularly at epidemic changepoints. Using a variety of scoring rules, we show that ensemble approaches outperformed all individual models, providing higher performance at a 21-day window than the corresponding individual models at 14-days. We introduce a modelling structure used by public health officials in England in 2022 to inform NHS healthcare strategy and policy decision making. This paper explores the significance of ensemble methods to improve forecasting performance and how novel syndromic surveillance can be practically applied in epidemic forecasting.


page 10

page 11

page 15

page 16

page 25


Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models

Background: Seasonal influenza causes a substantial burden on healthcare...

Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK

Following the UK Government's Living with COVID-19 Strategy and the end ...

Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions

A common statistical problem is prediction, or forecasting, in the prese...

Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves

We investigate ensembling techniques in forecasting and examine their po...

Forecasting local hospital bed demand for COVID-19 using on-request simulations

For hospitals, realistic forecasting of bed demand during impending epid...

A unified machine learning approach to time series forecasting applied to demand at emergency departments

There were 25.6 million attendances at Emergency Departments (EDs) in En...

An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions

The availability of intensive care beds during the Covid-19 epidemic is ...

Please sign up or login with your details

Forgot password? Click here to reset