tetano
Editor, Senior Moderator
Epidemics
. 2022 May 23;39:100580.
doi: 10.1016/j.epidem.2022.100580. Online ahead of print.
Gecko: A time-series model for COVID-19 hospital admission forecasting
Mark J Panaggio[SUP] 1 [/SUP], Kaitlin Rainwater-Lovett[SUP] 2 [/SUP], Paul J Nicholas[SUP] 2 [/SUP], Mike Fang[SUP] 2 [/SUP], Hyunseung Bang[SUP] 2 [/SUP], Jeffrey Freeman[SUP] 2 [/SUP], Elisha Peterson[SUP] 2 [/SUP], Samuel Imbriale[SUP] 3 [/SUP]
Affiliations
Abstract
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.
Keywords: COVID-19; Coronavirus disease; Forecasting; Hospitalization; SARIMA; SARS-CoV-2; Time-series model.
. 2022 May 23;39:100580.
doi: 10.1016/j.epidem.2022.100580. Online ahead of print.
Gecko: A time-series model for COVID-19 hospital admission forecasting
Mark J Panaggio[SUP] 1 [/SUP], Kaitlin Rainwater-Lovett[SUP] 2 [/SUP], Paul J Nicholas[SUP] 2 [/SUP], Mike Fang[SUP] 2 [/SUP], Hyunseung Bang[SUP] 2 [/SUP], Jeffrey Freeman[SUP] 2 [/SUP], Elisha Peterson[SUP] 2 [/SUP], Samuel Imbriale[SUP] 3 [/SUP]
Affiliations
- PMID: 35636313
- DOI: 10.1016/j.epidem.2022.100580
Abstract
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.
Keywords: COVID-19; Coronavirus disease; Forecasting; Hospitalization; SARIMA; SARS-CoV-2; Time-series model.