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 .
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 regions, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters. Whereas for Berlin, an ICU rate of 6 \%, a time lag of 6 days, and an average 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 average stay (4 and 8 days) in ICU 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. Thus, the model can help to predict a potential exceedance of ICU capacity. Although our predictions are based on small data sets and disregard non-stationary dynamics, our model is simple, robust, and can be used in early phases of the disease when data are scarce .
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Covid-19
COVID-19
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derive
infer
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rates. We evaluate
of infections. We have evaluated
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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
three regions, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters. Whereas for Berlin,
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5
6
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5 days
6 days,
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14
12
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, for Lombardy and Madrid the ICU rate was higher (18\% and 15\%) and the time lag (0
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is in accord with independent estimates. Our model is
3 days) and the average stay (4 and 8 days) in ICU shorter. The region-specific models are
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either
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. 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
or linear growth. Thus, the model can
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a small data set,
small data sets and
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and have a number of assumptions, especially an exponential development of cases,
D
adaptable
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up-dated when further data become available
used in early phases of the disease when data are scarce