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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/2170| Covid-19 -- A simple statistical model for predicting ICU load in exponential phases of the disease | |
| Matthias Ritter. John-Dylan Haynes. Kerstin Ritter. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://arxiv.org/pdf/2004.03384v1.pdf | |
| 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 derive 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 rates. We evaluate our model for public data from Berlin, Germany, by first estimating the model parameters (i.e., time lag and average stay in ICU) for March 2020 and then using an exponential model to predict the future ICU load for April and May 2020. Assuming an ICU rate of 5%, a time lag of 5 days and an average stay of 14 days in ICU provide the best fit of the data and is in accord with independent estimates. Our model is then used to predict future ICU load assuming a continued exponential phase with varying growth rates (0-15%). For example, based on our parameters the model predicts that the number of ICU patients at the end of May would be 246 if the exponential growth were to continue at a rate of 3%, 1,056 if the growth rate were 5% and 3,758 if the growth rate were 7%. The model can be adjusted as estimates of parameters develop and can thus also help to predict a potential exceedance of ICU capacity. Although our predictions are based on a small data set, disregard non-stationary dynamics, and have a number of assumptions, especially an exponential development of cases, our model is simple, robust, adaptable and can be up-dated when further data become available. | |
| arxiv.org | |
| 2020 | |
| Artículo | |
| https://arxiv.org/pdf/2004.03384v1.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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