Prediction of hospital bed capacity during the COVID-19 pandemic

Background: Prediction of the necessary capacity of beds by ward type (e.g. ICU) is essential for planning purposes during epidemics, such as the COVID− 19 pandemic. The COVID− 19 taskforce within the Ghent University hospital made use of ten-day forecasts on the required number of beds for COVID− 19 patients across different wards.Methods: The planning tool combined a Poisson model for the number of newly admitted patients on each day with a multistate model for the transitions of admitted patients to the different wards, discharge or death. These models were used to simulate the required capacity of beds by ward type over the next 10 days, along with worst-case and best-case bounds.Results: Overall, the models resulted in good predictions of the required number of beds across different hospital wards. Short-term predictions were especially accurate as these are less sensitive to sudden changes in number of beds on a given ward (e.g. due to referrals). Code snippets and details on the set-up are provided to guide the reader to apply the planning tool on one’s own hospital data.Conclusions: We were able to achieve a fast setup of a planning tool useful within the COVID− 19 pandemic, with a fair prediction on the needed capacity by ward type. This methodology can also be applied for other epidemics.Background: Prediction of the necessary capacity of beds by ward type (e.g. ICU) is essential for planning purposes during epidemics, such as the COVID− 19 pandemic. The COVID− 19 taskforce within the Ghent University hospital made use of ten-day forecasts on the required number of beds for COVID− 19 patients across different wards.Methods: The planning tool combined a Poisson model for the number of newly admitted patients on each day with a multistate model for the transitions of admitted patients to the different wards, discharge or death. These models were used to simulate the required capacity of beds by ward type over the next 10 days, along with worst-case and best-case bounds.Results: Overall, the models resulted in good predictions of the required number of beds across different hospital wards. Short-term predictions were especially accurate as these are less sensitive to sudden changes in number of beds on a given ward (e.g. due to referrals). Code snippets and details on the set-up are provided to guide the reader to apply the planning tool on one’s own hospital data.Conclusions: We were able to achieve a fast setup of a planning tool useful within the COVID− 19 pandemic, with a fair prediction on the needed capacity by ward type. This methodology can also be applied for other epidemics.A