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A two-stage stochastic variational inequality model for storage and dynamic distribution of medical supplies in epidemic management.
Li, Min; Zhang, Chao; Ding, Mingxv; Lv, Ruipu.
  • Li M; Department of Applied Mathematics Beijing Jiaotong University, Beijing 100044, China.
  • Zhang C; Department of Applied Mathematics Beijing Jiaotong University, Beijing 100044, China.
  • Ding M; Department of Applied Mathematics Beijing Jiaotong University, Beijing 100044, China.
  • Lv R; Department of Applied Mathematics Beijing Jiaotong University, Beijing 100044, China.
Appl Math Model ; 102: 35-61, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1446414
ABSTRACT
The storage and distribution of medical supplies are important parts of epidemic prevention and control. This paper first proposes a new nonsmooth two-stage stochastic equilibrium model of medical supplies in epidemic management. The first stage addresses the storage in the pre-disaster phase, and the second stage focuses on the dynamic distribution by enrolling competitions among multiple hospitals over a period of time in the post-disaster phase. The uncertainties are the numbers of infected people treated in multiple hospitals during the period of time, which are time-varying around a nominal distribution predicted by historical experience. The two-stage stochastic equilibrium model is further approximated and transformed to a monotone two-stage stochastic variational inequality (SVI) model that is computationally tractable, with the aid of a smooth approximation technique. We employ the progressive hedging method (PHM) to solve a case study in the city of Wuhan in China suffered from the COVID-19 pandemic. Numerical results are presented to demonstrate the effectiveness of the proposed model in planning the storage and dynamic distribution of medical supplies in epidemic management.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Topics: Long Covid Language: English Journal: Appl Math Model Year: 2022 Document Type: Article Affiliation country: J.apm.2021.09.033

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Topics: Long Covid Language: English Journal: Appl Math Model Year: 2022 Document Type: Article Affiliation country: J.apm.2021.09.033