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Halogenated Agent Delivery in Porcine Model of Acute Respiratory Distress Syndrome via an Intensive Care Unit Type Device
Published on: September 24, 2020
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[Forecasting models to guide intensive care COVID-19 capacities in Germany]
Marlon Grodd1, Lukas Refisch1, Fabian Lorenz1
1Institut für Medizinische Biometrie und Statistik, Medizinische Fakultät und Universitätsklinikum, Albert-Ludwigs-Universität Freiburg, Freiburg, Deutschland.
Medizinische Klinik, Intensivmedizin Und Notfallmedizin
|March 10, 2022
Summary
Time-series forecasting models predict intensive care unit (ICU) bed capacity for coronavirus disease 2019 (COVID-19) by analyzing new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and ICU occupancy rates in Germany.
Area of Science:
- Epidemiology
- Public Health
- Mathematical Modeling
Context:
- Intensive care unit (ICU) bed capacity management is critical during pandemics like coronavirus disease 2019 (COVID-19).
- Predicting future ICU occupancy requires understanding factors like new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, reporting delays, and vaccination rates.
- ICU discharge and mortality rates significantly influence current and future COVID-19 bed occupancy.
Purpose:
- To forecast intensive care unit (ICU) occupancy by COVID-19 patients in Germany.
- To provide timely 20-day forecasts to decision-makers at various governmental levels.
- To enable proactive identification of potential ICU capacity limitations.
Summary:
- Epidemic SEIR (susceptible, exposed, infection, recovered) models and multiple regression models are used to analyze daily data on new SARS-CoV-2 infections and ICU occupancy in Germany.
- Forecast results are generated to predict the immediate trend of COVID-19 ICU occupancy.
- These forecasts are crucial for guiding ICU bed capacity planning.
Impact:
- Forecasts inform decision-makers, enabling early identification of potential ICU capacity limitations.
- Facilitates the implementation of short-term solutions, such as supraregional patient transfers.
- Supports effective resource allocation and management of healthcare capacity during the COVID-19 pandemic.
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