  We consider a region containing several hospitals collaborating to treat
patients during an infectious outbreak. Collaboration occurs through the
dynamic allocation of hospital bed capacity to infectious patients and by
assigning infectious patients to specific hospitals. Scaling up capacity for
infectious patients means that beds are removed from regular care, which can
only be done simultaneously for all beds in a room. Moreover, as opening rooms
for infectious patients takes some lead time, we have to decide on preparing to
open rooms for infectious patients ahead of time. We apply a stochastic direct
lookahead approach. Each day, we make decisions on room allocation and patient
assignment based on the solutions of two stochastic programs with scenarios
using short-time forecasts of the number of infectious hospitalizations in the
region and the bed occupancy in each collaborating hospital. We aim to balance
costs for bed shortages and unutilized beds for infectious patients and opening
and closing rooms. We demonstrate that a stochastic lookahead is superior to a
deterministic one. We furthermore compare our solution approach with two
heuristic strategies in a simulation study based on historical COVID-19 data of
a region with three hospitals in the Netherlands. In one strategy, hospitals
decide on their capacity allocation individually. In another strategy, we
assume a pandemic unit, where one hospital is designated to take all regional
infectious patients until full. The numerical results show that our stochastic
direct lookahead approach considerably outperforms these heuristics.
