Your browser doesn't support javascript.
Gecko: A time-series model for COVID-19 hospital admission forecasting.
Panaggio, Mark J; Rainwater-Lovett, Kaitlin; Nicholas, Paul J; Fang, Mike; Bang, Hyunseung; Freeman, Jeffrey; Peterson, Elisha; Imbriale, Samuel.
  • Panaggio MJ; Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America. Electronic address: mark.panaggio@jhuapl.edu.
  • Rainwater-Lovett K; Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
  • Nicholas PJ; Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
  • Fang M; Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
  • Bang H; Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
  • Freeman J; Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
  • Peterson E; Johns Hopkins University Applied Physics Laboratory, 11100 Johns Hopkins Road, Laurel, MD 20723, United States of America.
  • Imbriale S; Office of the Assistant Secretary for Preparedness and Response, U.S. Department of Health and Human Services, Washington, DC, United States of America.
Epidemics ; 39: 100580, 2022 06.
Article in English | MEDLINE | ID: covidwho-1907009
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Lizards Type of study: Experimental Studies / Observational study / Prognostic study Limits: Animals / Humans Country/Region as subject: North America Language: English Journal: Epidemics Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Lizards Type of study: Experimental Studies / Observational study / Prognostic study Limits: Animals / Humans Country/Region as subject: North America Language: English Journal: Epidemics Year: 2022 Document Type: Article