ArticleEpidemics2022
Gecko: A time-series model for COVID-19 hospital admission forecasting.
Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A modular approach to forecasting COVID-19 hospital bed occupancy.Communications medicine · 2025Article
- Exploring different modelling approaches to forecast the community acute respiratory infections burden in children: an Italian epidemiological time series study.BMC public health · 2025Article
- Projecting COVID-19 intensive care admissions for policy advice, the Netherlands, February 2020 to January 2021.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024Article
- The accuracy of forecasted hospital admission for respiratory tract infections in children aged 0-5 years for 2017/2023.Frontiers in pediatrics · 2024Article
- A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis.Healthcare analytics (New York, N.Y.) · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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