ArticlePloS one2021
Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Optimizing Emergency Response in Hospitals: A Systematic Review of Surge Capacity Planning and Crisis Resource Management.Healthcare (Basel, Switzerland) · 2025Review
- Article
- A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets.JMIR formative research · 2024Article
- Automatic Forecast of Intensive Care Unit Admissions: The Experience During the COVID-19 Pandemic in Italy.Journal of medical systems · 2023Article
- Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.PloS one · 2023Article
- Nonlinear time-series forecasts for decision support: short-term demand for ICU beds in Santiago, Chile, during the 2021 COVID-19 pandemic.International transactions in operational research : a journal of The International Federation of Operational Research Societies · 2022Article
- Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning-A Scoping Review.International journal of environmental research and public health · 2022Article
- Hospital length of stay for COVID-19 patients: a systematic review and meta-analysis.Multidisciplinary respiratory medicine · 2022Article
- A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics.PloS one · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the pressure on healthcare authorities to assess whether hospital capacity allows properly responding to outbreaks such as COVID-19, there is a need for simple, data-driven methods that may provide accurate forecasts of hospital bed demand. This study applies growth models to forecast the demand for Intensive Care Unit admissions in Italy during COVID-19. We show that, with only some mild assumptions on the functional form and using short time-series, the model fits past data well and can accurately forecast demand fourteen days ahead (the mean absolute percentage error (MAPE) of the cumulative fourteen days forecasts is 7.64). The model is then applied to derive regional-level forecasts by adopting hierarchical methods that ensure the consistency between national and regional level forecasts. Predictions are compared with current hospital capacity in the different Italian regions, with the aim to evaluate the adequacy of the expansion in the number of beds implemented during the COVID-19 crisis.
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Registered trials
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