alexa The Zoonoses Data Collection in Italy: An Expert System
ISSN: 2157-7579

Journal of Veterinary Science & Technology
Open Access

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Research Article

The Zoonoses Data Collection in Italy: An Expert System for Data Quality Management and Improvement

Iannetti S1*, Cioci D2, Falcone MG3 and Colangeli P2

1Veterinary Medicine, Livestock Research Institute of Abruzzo and Molise G. Caporale, Campo Boario Teramo, Italy

2Computer Science, Institute Experimental Zooprofilattico and Molise G. Caporale, Campo Boario Teramo, Italy

3Veterinary Medicine, Ministry of Health, Office III DGSAF, Rome, Italy

*Corresponding Author:
Iannetti S
Veterinarian, Doctor in Veterinary Medicine
Livestock Research Institute of Abruzzo and Molise G. Caporale
Campo Boario Teramo, Italy
Tel: 00390861332243
E-mail: [email protected]

Received Date: February 17, 2017 Accepted Date: March 15, 2017 Published Date:March 17, 2017

Citation: Iannetti S, Cioci D, Falcone MG, Colangeli P (2017) The Zoonosis Data Collection in Italy: An Expert System for Data Quality Management and Improvement. J Vet Sci Technol 8: 431. doi: 10.4262/2157-7579.1000431

Copyright: © 2017 Iannetti S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

The need to improve the quality of data for a better analysis and understanding of zoonoses’ trend at country level has been increased year by year both by the EFSA and by reporting countries. In the framework of an EFSA’s Grant project, aimed to complement the zoonoses historical database, an expert system based on logical rules of truth tables was put in place within the Italian information system for zoonoses data collection (SINZoo). During data entry, the truth tables check that, for each zoonoses, the combination of the area of interest, each possible sampling context, stage and sampling unit has been entered correctly, thus avoiding inconsistent data. Each combination available in the truth tables indicates the context, the stage, the sampling unit allowed for each zoonosis in a specific area and for a category of species. The goal of the project was achieved for most of the information to be retrieved: the 89% and the 83% of sampling contexts and stages respectively and the 100% of the other information were retrieved. To date, the truth tables developed for specific zoonoses have become integral part of SINZoo, allowing to avoid mistakes during data reporting. Data quality is the pillar for any analysis and to perform risk analysis: the logical rules of truth tables can be implemented in other information systems involved in data collection in the field of animal health and food safety, increasing both the consistence and the coherence of the data reported.

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