Allocation of emergency medical resources for epidemic diseases considering the heterogeneity of epidemic areas

Abstract

Background: The resources available to fight an epidemic are typically limited, and the time and effort required to control it grow as the start date of the containment effort are delayed. When the population is afflicted in various regions, scheduling a fair and acceptable distribution of limited available resources stored in multiple emergency resource centers to each epidemic area has become a serious problem that requires immediate resolution.

Methods: This study presents an emergency medical logistics model for rapid response to public health emergencies. The proposed methodology consists of two recursive mechanisms: (1) time-varying forecasting of medical resources and (2) emergency medical resource allocation. Considering the epidemic's features and the heterogeneity of existing medical treatment capabilities in different epidemic areas, we provide the modified susceptible-exposed-infected-recovered (SEIR) model to predict the early stage emergency medical resource demand for epidemics. Then we define emergency indicators for each epidemic area based on this. By maximizing the weighted demand satisfaction rate and minimizing the total vehicle travel distance, we develop a bi-objective optimization model to determine the optimal medical resource allocation plan.

Results: Decision-makers should assign appropriate values to parameters at various stages of the emergency process based on the actual situation, to ensure that the results obtained are feasible and effective. It is necessary to set up an appropriate number of supply points in the epidemic emergency medical logistics supply to effectively reduce rescue costs and improve the level of emergency services.

Conclusions: Overall, this work provides managerial insights to improve decisions made on medical distribution as per demand forecasting for quick response to public health emergencies.

Keywords: emergency medical resource distribution decision model; epidemic diseases; resource allocation; time-varying demand forecasting model; weighting sum method; ε-constraint method.

Figure 1

Schematic diagram of state transition.

Figure 2

Epidemic trend in scenario 1 (Asymptomatic not included) of epidemic area 1.

Figure 3

Epidemic trend in scenario 2 (Asymptomatic included) of epidemic area 1.

Figure 4

Impact of different minimum satisfaction rates on emergency rescue targets of the ε-constraint method.

Figure 5

Impact of different minimum satisfaction rates on emergency rescue targets of the Weighting sum method.