Animals (Jun 2020)

A New Information System for the Management of Non-Epidemic Veterinary Emergencies

  • Luigi Possenti,
  • Lara Savini,
  • Annamaria Conte,
  • Nicola D'Alterio,
  • Maria Luisa Danzetta,
  • Alessio Di Lorenzo,
  • Maria Nardoia,
  • Paolo Migliaccio,
  • Susanna Tora,
  • Paolo Dalla Villa

DOI
https://doi.org/10.3390/ani10060983
Journal volume & issue
Vol. 10, no. 6
p. 983

Abstract

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The Italian National Veterinary Services, public health professionals, and policy makers are asked to participate at different levels in the decision-making process for the management of non-epidemic emergencies. A decision support system offering the different administrative and operational emergency management levels with a spatial and decisional tool to be used in the case of natural disasters is still missing at the national level. Within this context, the Italian General Directorate for Animal Health of the Ministry of Health funded a research project for the implementation of a new Veterinary Information System for Non-Epidemic Emergencies (SIVENE), an innovative real-time decision support tool for emergency response in a disaster management scenario. SIVENE was developed according to a multi-layer architecture with four integrated components: the database layer, which was implemented by an RDBMS Oracle 11 g; the ReST service layer, which was created using J2EE, Spring, and MyBatis technologies; the web application (business framework and user interface), which was developed in Angular4 framework using TypeScript language; and the web Geographic Information Systems (GIS), which was realized through the implementation of a geodatabase in Oracle RDBMS 11 g. This system allows us to build up and dynamically create a set of dedicated checklists to be used in the field when gathering the information needed for the management of non-epidemic emergencies; employ the application on mobile devices, such as tablets and smartphones; and use the web GIS to manage and visualize data of veterinary interest and territorial maps of risk and damage.

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