Humanitarian Medical Relief Allocation In Public Health Emergency

Emerging and re-emerging epidemic diseases pose an on-going threat to global public health security. In addition to health threats and economic losses, public health emergencies also result in psychological suffering. This research underscores the importance of humanitarian medical alloca-tion for public health emergencies, and aims to explore a novel analytical approach for improving humanitarian allocation of medical reliefs for response to unconventional large-scale epidemics, and draw managerial insights for health care practice.Specifically, three sub-topics are conducted:(1) Single-area dynamic allocation model of humanitarian medical reliefs. In the environment with uncertain demand, this sub-research presents a temporal allocation model of medical reliefs for response to large-scale epidemic outbreaks. The proposed stochastic dynamic programming approach is developed based on the time-varying relationship among the inventory, consumption and supply of medical reliefs. This sub-research provides a general closed-form of the optimal allocation policy in each time period, and several properties of the problem and its optimal policy are derived. In addition, a case study based on a real epidemic outbreak is conducted and the relations between the optimal policy and each parameter are discussed. These results highlight some managerial implications.(2) Multi-area allocation model of humanitarian medical reliefs. The proposed methodology in this sub-research consists of two recursive mechanisms:(a) the time-varying forecasting of medical relief demand and (b) relief distribution. The medical demand associated with each epidemic area is forecast according to a modified susceptible-exposed-infected-recovered (SEIR) 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 the applicability and potential advantages of the consideration of survivor psychology.(3) Cross-sector cooperation in humanitarian medical allocation. In this sub-research of cross-sector cooperation in allocation, a series of cross-sector decision models are developed to discuss different types of cooperation and information sharing between public and private sectors. The basic model, which consists of a public sector (usually the government) and a private sector, is formulated to obtain the optimal decisions of the two sectors. Then this research presents three more cooperation mechanisms:semi-cooperation with a private leader, semi-cooperation with a government leader, and full cooperation. The optimal solutions of these four models are provided and compared. By solving and comparing their optimal solutions, this sub-research makes the first step to understand the differences among these four mechanisms. The results illustrate that full cooperation is not always the best choice, while semi-cooperation with information sharing would also achieve potential advantages, even if two sectors made their own decisions separately.This research contributes to literature in the following ways:(1) This interdisciplinary study contributes to the fields of public health and humanitarian lo-gistics (emergency logistics). Humanitarian medical allocation differs from general humanitarian allocation problem and related business logistics problems in that the former involves many chal-lenges that increase the complexity and difficulty of solving logistical problems.(2) The stochastic dynamic programming model proposed in Chapter 3 characterizes the tem-poral allocation problem of medical supply based on the trend of epidemic disease spreading. This new formulation is closer to the real logistics practice during epidemic outbreaks. In addition, this research obtains a general form of the optimal solution of the proposed stochastic dynamic model.(3) In Chapter 4, the modified SEIR model contributes to forecasting by considering not only physical factors, such as the differences in the infection conditions of survivors and the spatial interaction relationships among epidemic areas, but also the psychological demand of exposed and undiagnosed individuals.(4) In the distribution model in Chapter 4, psychological fragility is formulated and discussed in detail, unlike in previous studies. The relationship between emergency medical logistics and the psychological effects on affected people is highlighted as well.(5) Chapter 5 proposes and compares four optimization models, including one allocation model without cross-sector cooperation and three cooperation models with different cooperation mecha-nisms. It illustrates the value of cross-sector cooperation and information sharing between public and private sectors when making humanitarian allocation decisions.