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Strategies evaluation in environmental conditions by symbolic data analysis: application in medicine and epidemiology to trachoma

Guinot, Christiane; Malvy, Denis; Schémann, Jean-François; Afonso, Filipe; Haddad, Raja; Diday, Edwin (2015), Strategies evaluation in environmental conditions by symbolic data analysis: application in medicine and epidemiology to trachoma, Advances in Data Analysis and Classification, 9, 1, p. 107-119. http://dx.doi.org/10.1007/s11634-015-0201-2

Type
Article accepté pour publication ou publié
Date
2015
Journal name
Advances in Data Analysis and Classification
Volume
9
Number
1
Publisher
Springer
Pages
107-119
Publication identifier
http://dx.doi.org/10.1007/s11634-015-0201-2
Metadata
Show full item record
Author(s)
Guinot, Christiane
Malvy, Denis
Schémann, Jean-François
Afonso, Filipe
Haddad, Raja
Diday, Edwin
Abstract (EN)
Trachoma, caused by repeated ocular infections with Chlamydia trachomatis whose vector is a fly, is an important cause of blindness in the world. We are presenting here an application of the Symbolic Data Analysis approach to an interventional study on trachoma conducted in Mali. This study was conducted to choose among three antibiotic strategies those with the best cost-effectiveness ratio and to find the demographic and environmental parameters on which we could try to intervene. The Symbolic Data Analysis approach aims at studying classes of individuals considered as new units. These units are described by variables whose values express for each class the variation of the values taken by each of its individuals. Finally, the results obtained are compared to those previously provided by multiple logistic regression analysis. Symbolic Data Analysis actually provides a new perspective on this study and suggests that some demographic, economics and environmental parameters are related to the disease and its evolution during the treatment, whatever the strategy. Moreover, it is shown that the efficiency of each strategy depends on environmental parameters.
Subjects / Keywords
Symbolic Data Analysis; Multiple logistic regression; Trachoma

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