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Allocation of emergency medical resources for epidemic diseases considering the heterogeneity of epidemic areas.
Hu, Bin; Jiang, Guanhua; Yao, Xinyi; Chen, Wei; Yue, Tingyu; Zhao, Qitong; Wen, Zongliang.
Afiliación
  • Hu B; School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Jiang G; School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Yao X; School of Management, Xuzhou Medical University, Xuzhou, China.
  • Chen W; School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Yue T; School of Public Health, Xuzhou Medical University, Xuzhou, China.
  • Zhao Q; Department of Logistics and Supply Chain Management School of Business, Singapore University of Social Science, Singapore, Singapore.
  • Wen Z; School of Management, Xuzhou Medical University, Xuzhou, China.
Front Public Health ; 11: 992197, 2023.
Article en En | MEDLINE | ID: mdl-36908482
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Urgencias Médicas / Epidemias Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Front Public Health Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Urgencias Médicas / Epidemias Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Front Public Health Año: 2023 Tipo del documento: Article País de afiliación: China
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