{"title":"Grey models for data analysis and decision-making in uncertainty during pandemics","authors":"","doi":"10.1016/j.ijdrr.2024.104881","DOIUrl":null,"url":null,"abstract":"<div><div>We analyse in this study quite a few uncertain decision-making situations concomitant to a pandemic spread and some of the models using grey theory to deal with such situations. We present <em>four</em> stylised models to tackle different decision support situations at the time of any pandemic crisis, such as COVID-19. <em>Eight</em> diverse problems or situations of risk mitigation and decision-making under uncertainties are proposed in this research and the methodologies employing grey incidence analysis, grey clustering, grey prediction, and grey programming models are offered. Numerical illustrations of the applications of these methodologies are also typified in this paper for future development. A practical application of the widely used grey prediction model is also demonstrated in this study to predict the number of COVID-19 infections and fatalities of <em>five</em> Indian states during a considered period of study. The implications of the study are for data scientists, decision-makers, and practitioners to use the benefits of grey theory in dealing with various situations of uncertainty, such as a spread of a pandemic.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212420924006435","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
We analyse in this study quite a few uncertain decision-making situations concomitant to a pandemic spread and some of the models using grey theory to deal with such situations. We present four stylised models to tackle different decision support situations at the time of any pandemic crisis, such as COVID-19. Eight diverse problems or situations of risk mitigation and decision-making under uncertainties are proposed in this research and the methodologies employing grey incidence analysis, grey clustering, grey prediction, and grey programming models are offered. Numerical illustrations of the applications of these methodologies are also typified in this paper for future development. A practical application of the widely used grey prediction model is also demonstrated in this study to predict the number of COVID-19 infections and fatalities of five Indian states during a considered period of study. The implications of the study are for data scientists, decision-makers, and practitioners to use the benefits of grey theory in dealing with various situations of uncertainty, such as a spread of a pandemic.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.