Journal
RISK ANALYSIS
Volume 41, Issue 5, Pages 814-830Publisher
WILEY
DOI: 10.1111/risa.13678
Keywords
Causality; coronavirus; decision making; quantitative analysis; risk quadruplet; US county-level data
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This article introduces a risk-based decision-making methodology to assist policymakers in taking timely action during major disasters. By analyzing data, it uncovers causal relationships between variables, providing decision support for policymakers.
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.
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