Foresighted medical resources allocation during an epidemic outbreak

Yuqing Pan, T.C.E. Cheng, Yuxuan He, Chi To Ng and Suresh Sethi

Transportation Research Part E: Logistics and Transportation Review, 2022, vol. 164, issue C

Abstract: While some reports show that the existing real-life medical resources allocations during epidemic outbreaks are myopic, some experts claim that medical resources allocations based on foresighted future allocations might enable a better balance of supply and demand. To investigate this claim, we develop a foresighted medical resources allocation model to help governments manage large-scale epidemic outbreaks. We formulate a demand forecasting model with a general demand forecasting function based on the last-period demands, extra demand caused by the last-period unfulfilled demand, and uncertain demand. In the foresighted allocation model, the government decides the current-period allocation based on the foresighted demand, which considers the last-period area demand and uncertain demand from the current period to the end of a planning horizon, using a stochastic dynamic program. We find that the optimal allocation is a function of the allocation capacity in each period. The optimal foresighted allocation is always higher than the optimal static (one-period) allocation and decreases with allocation capacity. When the allocation capacity is sufficiently large, the foresighted demand is close to the static demand. Besides, if the cost of oversupply is close to zero, the optimal allocations for both the foresighted allocation and one-period models are the allocation capacity. Our results provide useful managerial implications for a government contemplating medical resources allocation in response to an epidemic outbreak.

Keywords: Health care; Emergency logistics; Stochastic dynamic programming (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.tre.2022.102762

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