Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand.

ABSTRACT: Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand's unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our r

SUBMITTER: Plank MJ 

PROVIDER: S-EPMC10798620 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature