COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
Abstract One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensiv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-83853-2 |
| _version_ | 1818752035096363008 |
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| author | Matthias Ritter Derek V. M. Ott Friedemann Paul John-Dylan Haynes Kerstin Ritter |
| author_facet | Matthias Ritter Derek V. M. Ott Friedemann Paul John-Dylan Haynes Kerstin Ritter |
| author_sort | Matthias Ritter |
| collection | DOAJ |
| description | Abstract One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0–15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease. |
| first_indexed | 2024-12-18T04:45:03Z |
| format | Article |
| id | doaj.art-b4045b3cd80948ccb49ee74e3696e398 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-18T04:45:03Z |
| publishDate | 2021-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b4045b3cd80948ccb49ee74e3696e3982022-12-21T21:20:37ZengNature PortfolioScientific Reports2045-23222021-03-0111111210.1038/s41598-021-83853-2COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the diseaseMatthias Ritter0Derek V. M. Ott1Friedemann Paul2John-Dylan Haynes3Kerstin Ritter4Faculty of Life Sciences, Humboldt-Universität zu BerlinNeurology Clinic with Stroke Unit and Early Rehabilitation, Unfallkrankenhaus BerlinCharité-Universitätsmedizin Berlin and Berlin Institute of Health (BIH)Charité-Universitätsmedizin Berlin and Berlin Institute of Health (BIH)Charité-Universitätsmedizin Berlin and Berlin Institute of Health (BIH)Abstract One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0–15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease.https://doi.org/10.1038/s41598-021-83853-2 |
| spellingShingle | Matthias Ritter Derek V. M. Ott Friedemann Paul John-Dylan Haynes Kerstin Ritter COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease Scientific Reports |
| title | COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease |
| title_full | COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease |
| title_fullStr | COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease |
| title_full_unstemmed | COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease |
| title_short | COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease |
| title_sort | covid 19 a simple statistical model for predicting intensive care unit load in exponential phases of the disease |
| url | https://doi.org/10.1038/s41598-021-83853-2 |
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