Orsi Mark J, Santos Joost R
1 Center for Risk Management of Engineering SystemsUniversity of Virginia Charlottesville VA 22904-4160 USA.
2 Department of Engineering Management and Systems EngineeringThe George Washington University Washington DC 20052 USA.
IEEE Trans Syst Man Cybern A Syst Hum. 2009 Nov 6;40(2):301-305. doi: 10.1109/TSMCA.2009.2033032. eCollection 2010 Mar.
A pandemic outbreak is one of the major planning scenarios considered by emergency-preparedness policymakers. The consequences of a pandemic can significantly affect and disrupt a large spectrum of workforce sectors in today's society. This paper, motivated by the impact of a pandemic, extends the formulation of the dynamic inoperability input-output model (DIIM) to account for economic perturbations resulting from such an event, which creates a time-varying and probabilistic inoperability to the workforce. A pandemic is a unique disaster, because the majority of its direct impacts are workforce related and it does not create significant direct impact to infrastructure. In light of this factor, this paper first develops a method of translating unavailable workforce into a measure of economic-sector inoperability. While previous formulations of the DIIM only allowed for the specification of an initial perturbation, this paper incorporates the fact that a pandemic can cause direct effects to the workforce over the recovery period. Given the uncertainty associated with the impact of a pandemic, this paper develops a simulation framework to account for the possible variations in realizations of the pandemic. The enhancements to the DIIM formulation are incorporated into a MatLab program and then applied to a case study to simulate a pandemic scenario in the Commonwealth of Virginia.
大流行疫情是应急准备政策制定者考虑的主要规划情景之一。大流行的后果会对当今社会的众多劳动力部门产生重大影响并造成干扰。本文受大流行影响的启发，扩展了动态不可用性投入产出模型（DIIM）的公式，以考虑此类事件导致的经济扰动，这种扰动会给劳动力带来随时间变化的概率性不可用性。大流行是一种独特的灾难，因为其大部分直接影响都与劳动力相关，且不会对基础设施造成重大直接影响。鉴于这一因素，本文首先开发了一种将无法工作的劳动力转化为经济部门不可用性度量的方法。虽然之前的DIIM公式只允许指定初始扰动，但本文纳入了大流行在恢复期可能对劳动力产生直接影响这一事实。考虑到大流行影响的不确定性，本文开发了一个模拟框架，以考虑大流行实际情况的可能变化。对DIIM公式的改进被纳入一个MatLab程序，然后应用于一个案例研究，以模拟弗吉尼亚联邦州的大流行情景。
