Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, 3800, Australia.
Center for Technology and Systems Management, University of Maryland, College Park, MD, 20742, USA.
Risk Anal. 2021 May;41(5):814-830. doi: 10.1111/risa.13678. Epub 2021 Jan 14.
Either in the form of nature's wrath or a pandemic, catastrophes cause major destructions in societies, thus requiring policy and decisionmakers to take urgent action by evaluating a host of interdependent parameters, and possible scenarios. The primary purpose of this article is to propose a novel risk-based, decision-making methodology capable of unveiling causal relationships between pairs of variables. Motivated by the ongoing global emergency of the coronavirus pandemic, the article elaborates on this powerful quantitative framework drawing on data from the United States at the county level aiming at assisting policy and decision makers in taking timely action amid this emergency. This methodology offers a basis for identifying potential scenarios and consequences of the ongoing 2020 pandemic by drawing on weather variables to examine the causal impact of changing weather on the trend of daily coronavirus cases.
无论是自然灾害还是大流行病，灾难都会给社会带来重大破坏，因此需要政策制定者和决策者通过评估一系列相互依存的参数和可能的情景来采取紧急行动。本文的主要目的是提出一种新的基于风险的决策方法，该方法能够揭示变量对之间的因果关系。受冠状病毒大流行这一持续的全球紧急情况的启发，本文详细阐述了这一强大的定量框架，该框架利用美国县级数据，旨在帮助政策制定者和决策者在紧急情况下及时采取行动。该方法通过利用天气变量来研究天气变化对每日冠状病毒病例趋势的因果影响，为识别当前 2020 年大流行的潜在情景和后果提供了基础。
