Gecko: A time-series model for COVID-19 hospital admission forecasting
During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-06-01
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| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436522000299 |
| _version_ | 1811248956635611136 |
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| author | Mark J. Panaggio Kaitlin Rainwater-Lovett Paul J. Nicholas Mike Fang Hyunseung Bang Jeffrey Freeman Elisha Peterson Samuel Imbriale |
| author_facet | Mark J. Panaggio Kaitlin Rainwater-Lovett Paul J. Nicholas Mike Fang Hyunseung Bang Jeffrey Freeman Elisha Peterson Samuel Imbriale |
| author_sort | Mark J. Panaggio |
| collection | DOAJ |
| description | During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose. We evaluate its historical performance using metrics such as mean absolute error, predictive interval coverage, and weighted interval scores, and compare to alternative hospital admission forecasting models. We find that Gecko outperformed baseline approaches and was among the most accurate models for forecasting hospital admissions at the state and national levels from January–May 2021. This work suggests that simple statistical methods can provide a viable alternative to traditional epidemic models for short-term forecasting. |
| first_indexed | 2024-04-12T15:36:47Z |
| format | Article |
| id | doaj.art-9316d189a87248dbbfef2f79d743ace2 |
| institution | Directory Open Access Journal |
| issn | 1755-4365 |
| language | English |
| last_indexed | 2024-04-12T15:36:47Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-9316d189a87248dbbfef2f79d743ace22022-12-22T03:26:55ZengElsevierEpidemics1755-43652022-06-0139100580Gecko: A time-series model for COVID-19 hospital admission forecastingMark J. Panaggio0Kaitlin Rainwater-Lovett1Paul J. Nicholas2Mike Fang3Hyunseung Bang4Jeffrey Freeman5Elisha Peterson6Samuel Imbriale7Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America; Corresponding author.Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of AmericaJohns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of AmericaJohns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of AmericaJohns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of AmericaJohns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of AmericaJohns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of AmericaOffice of the Assistant Secretary for Preparedness and Response, U.S. Department of Health and Human Services, Washington, DC, United States of AmericaDuring the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose. We evaluate its historical performance using metrics such as mean absolute error, predictive interval coverage, and weighted interval scores, and compare to alternative hospital admission forecasting models. We find that Gecko outperformed baseline approaches and was among the most accurate models for forecasting hospital admissions at the state and national levels from January–May 2021. This work suggests that simple statistical methods can provide a viable alternative to traditional epidemic models for short-term forecasting.http://www.sciencedirect.com/science/article/pii/S1755436522000299SARS-CoV-2Coronavirus diseaseCOVID-19SARIMAForecastingTime-series model |
| spellingShingle | Mark J. Panaggio Kaitlin Rainwater-Lovett Paul J. Nicholas Mike Fang Hyunseung Bang Jeffrey Freeman Elisha Peterson Samuel Imbriale Gecko: A time-series model for COVID-19 hospital admission forecasting Epidemics SARS-CoV-2 Coronavirus disease COVID-19 SARIMA Forecasting Time-series model |
| title | Gecko: A time-series model for COVID-19 hospital admission forecasting |
| title_full | Gecko: A time-series model for COVID-19 hospital admission forecasting |
| title_fullStr | Gecko: A time-series model for COVID-19 hospital admission forecasting |
| title_full_unstemmed | Gecko: A time-series model for COVID-19 hospital admission forecasting |
| title_short | Gecko: A time-series model for COVID-19 hospital admission forecasting |
| title_sort | gecko a time series model for covid 19 hospital admission forecasting |
| topic | SARS-CoV-2 Coronavirus disease COVID-19 SARIMA Forecasting Time-series model |
| url | http://www.sciencedirect.com/science/article/pii/S1755436522000299 |
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