scholarly journals Vulnerability to COVID-19 in Pernambuco, Brazil: A geospatial evaluation supported by multiple-criteria decision aid methodology

2022 ◽  
Vol 17 (s1) ◽  
Author(s):  
Ciro José Jardim de Figueiredo ◽  
Caroline Maria de Miranda Mota ◽  
Amanda Gadelha Ferreira Rosa ◽  
Arthur Pimentel Gomes de Souza ◽  
Simone Maria da Silva Lima
Keyword(s):  
Risk Factors ◽  
Spatial Analysis ◽  
Environmental Factors ◽  
Decision Aid ◽  
Decision Rules ◽  
The State ◽  
Multiple Criteria ◽  
Learning Approach ◽  
Spatial Characteristics ◽  
Multiple Criteria Decision Aid

The paper presents an innovative application to identify areas vulnerable to coronavirus disease 2019 (COVID-19) considering a combination of spatial analysis and a multi-criteria learning approach. We applied this methodology in the state of Pernambuco, Brazil identifying vulnerable areas by considering a set of determinants and risk factors for COVID-19, including demographic, economic and spatial characteristics and the number of human COVID-19 infections. Examining possible patterns over a set number of days taking the number of cases recorded, we arrived at a set of compatible decision rules to explain the relation between risk factors and COVID-19 cases. The results reveal why certain municipalities are critically vulnerable to COVID-19 highlighting locations for which knowledge can be gained about environmental factors.

A Multiple criteria decision aid for personnel scheduling

1984 ◽  
Vol 16 (4) ◽  
pp. 299-307 ◽  
Author(s):  
C. Patrick Koelling ◽  
James E. Bailey
Keyword(s):  
Decision Aid ◽  
Multiple Criteria ◽  
Personnel Scheduling ◽  
Multiple Criteria Decision Aid

scholarly journals Transformation of criteria with a-priori chosen optimal values

2016 ◽  
Vol 57 ◽  
Author(s):  
Askoldas Podviezko ◽  
Valentinas Podvezko
Keyword(s):  
Decision Aid ◽  
A Priori ◽  
Multiple Criteria ◽  
Relative Importance ◽  
Decision Matrix ◽  
Best Value ◽  
Optimal Values ◽  
Multiple Criteria Decision Aid

Aim of multiple criteria decision-aid (MCDA) methods is to find the best alternative among the ones that are available or to rank alternatives in the order of preference. There are the following core pillars of the methods: the set of criteria and matrix with values of criteria that characterise the evaluated alternatives (decision matrix); and vector of weights that reflect relative importance of criteria. Usually, two types of criteria are used by researchers. Maximising criteria (e.g. profits) reflect a better situation whenever the larger value has been attained. While in case a criterion is minimising (e.g. costs), the better situation is reflected when its value is smaller. Such situations, when the best value of a criterion has a certain value, which differs from the maximal or the minimal, are usually not considered. This paper aims to fill this gap. Such criteria will be named as criteria with a-priori chosen optimal values. The aim of the paper is to propose appropriate types of transformation for criteria with a-priori chosen optimal values. Such transformations appear to be general and can be used with all three types of criteria.

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