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This work presents a novel model of emergency medical logistics for quick response to public health emergencies. The proposed methodology consists of two recursive mechanisms: (1) the time-varying forecasting of medical relief demand and (2) relief distribution. The medical demand associated with each epidemic area is forecast according to a modified susceptible-exposed-infected-recovered model. A linear programming approach is then applied to facilitate distribution decision-making. The physical and psychological fragility of affected people are discussed. Numerical studies are conducted. Results show that the consideration of survivor psychology significantly reduces the psychological fragility of affected people, but it barely influences physical fragility. [ABSTRACT FROM AUTHOR]

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. • Based on real practice in Hubei Province, China, during the COVID-19 outbreak. • Investigate foresighted medical resources allocation and static (myopic) allocation. • Develop and solve a stochastic dynamic program for the foresighted allocation. • Compare the two allocations and investigate conditions that they perform the same. • Provide useful managerial implications for medical resources allocation. [ABSTRACT FROM AUTHOR]