AUTHOR=Hu Bin , Jiang Guanhua , Yao Xinyi , Chen Wei , Yue Tingyu , Zhao Qitong , Wen Zongliang 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.992197

DOI=10.3389/fpubh.2023.992197

ISSN=2296-2565

ABSTRACT=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. 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 epidemics 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. The weighted sum approach and the epsilon–constraint method are used to solve the model. A numerical example of applying the proposed methodology to a specific disease outbreak is provided. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods. Our findings may aid decision-makers in preparing for a pandemic, such as how to dynamically allocate limited resources.