Abstract
We present a mathematical model that provides a real-time forecast of the number of infectious patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their length of stay in both the ward and the ICU as well as transfers of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital's data warehouse. Sustaining regular care during an infectious outbreak requires adequate capacity allocation to both infectious and regular care patients. We have developed a decision support system for central regional decision-making on opening and closing hospital care rooms for infectious patients and assigning new infectious patients to hospitals. We make decisions with a stochastic lookahead approach using stochastic programming with sample average approximation based on scenarios of the number of occupied infectious beds and infectious patients needing hospitalization. Currently, we are working on a concept called effective bed value, that quantifies the value of a bed in a hospital based on time-stamp data from regular patients: what does it cost a hospital to have to admit an infectious patient in terms of postponed regular care?
