摘要
In this paper, we develop a unique time-varying forecasting model for dynamic demand of medical resources based on a susceptible-exposed-infected-recovered （SEIR） influenza diffusion model. In this forecasting mechanism, medical resources allocated in the early period will take effect in subduing the spread of influenza and thus impact the demand in the later period. We adopt a discrete time-space network to describe the medical resources allocation process following a hypothetical influenza outbreak in a region. The entire medical resources allocation process is constructed as a multi-stage integer programming problem. At each stage, we solve a cost minimization sub-problem subject to the time-varying demand. The corresponding optimal allocation result is then used as an input to the control process of influenza spread, which in turn determines the demand for the next stage. In addition, we present a comparison between the proposed model and an empirical model. Our results could help decision makers prepare for a pandemic, including how to allocate limited resources dynamically.
In this paper, we develop a unique time-varying forecasting model for dynamic demand of medical resources based on a susceptible-exposed-infected-recovered （SEIR） influenza diffusion model. In this forecasting mechanism, medical resources allocated in the early period will take effect in subduing the spread of influenza and thus impact the demand in the later period. We adopt a discrete time-space network to describe the medical resources allocation process following a hypothetical influenza outbreak in a region. The entire medical resources allocation process is constructed as a multi-stage integer programming problem. At each stage, we solve a cost minimization sub-problem subject to the time-varying demand. The corresponding optimal allocation result is then used as an input to the control process of influenza spread, which in turn determines the demand for the next stage. In addition, we present a comparison between the proposed model and an empirical model. Our results could help decision makers prepare for a pandemic, including how to allocate limited resources dynamically.
基金
partially supported by the National Natural Science Foundation of China (No.71301076,71401075)
Natural Science Foundation of Jiangsu Province(BK20130771)
the Research Fund for the Doctoral Program of Higher Education of China(20133219120037)
the Zijin Intelligent Program(No. 2013-ZJ0211) of Nanjing University of Science and Technology
