Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand.
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 fore...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011752&type=printable |
| _version_ | 1827376318943068160 |
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| author | Michael J Plank Leighton Watson Oliver J Maclaren |
| author_facet | Michael J Plank Leighton Watson Oliver J Maclaren |
| author_sort | Michael J Plank |
| collection | DOAJ |
| description | 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 results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand. |
| first_indexed | 2024-03-08T12:08:08Z |
| format | Article |
| id | doaj.art-da499d7cb03647f4bffcaa8afd54f07b |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-08T12:08:08Z |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-da499d7cb03647f4bffcaa8afd54f07b2024-01-23T05:30:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-01-01201e101175210.1371/journal.pcbi.1011752Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand.Michael J PlankLeighton WatsonOliver J MaclarenNear-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 results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011752&type=printable |
| spellingShingle | Michael J Plank Leighton Watson Oliver J Maclaren Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. PLoS Computational Biology |
| title | Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. |
| title_full | Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. |
| title_fullStr | Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. |
| title_full_unstemmed | Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. |
| title_short | Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand. |
| title_sort | near term forecasting of covid 19 cases and hospitalisations in aotearoa new zealand |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011752&type=printable |
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