IntegraEPI: Epidemiologic Surveillance on the Grid

IntegraEPI: Epidemiologic Surveillance on the Grid

Fabricio Alves Barbosa da Silva (Universidad de Lisboa, Portugal), Henrique Fabricio Gagliardi (Instituto de Ensino Superior, Brazil), Eduardo Gallo (APRAESPI, Brazil), Maria Antonia Madope (Ford Foundation Alumni Association, Mozambique), Virgilio Cavicchioli Neto (Universidade Federal de Sao Paulo, Brazil), Ivan Torres Pisa (Universidade Federal de São Paulo, Brazil) and Domingos Alves (Universidade de São Paulo, Brasil)
DOI: 10.4018/978-1-60566-374-6.ch023
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Abstract

The authors present in this work a large-scale system for space-time visualization, monitoring, modeling and analysis of epidemic data using a Grid platform. This system, dubbed IntegraEPI, is capable to integrate data from heterogeneous databases related to epidemic analysis and to make available analytical and computational methods to increase the predicting capability of the public heath system, in order to optimize its activities and resources when dealing with epidemic outbreak and prevention. This system, differently of what has been proposed before, is integrated and consequently it enables the construction of detailed predictive models of the dynamics of disease spreading. With the help of IntegraEPI, Health authorities will be able to decide about a set of possible actions that will be previously tested in a virtual population interacting in an urban infrastructure, considering its environmental factors, and finally compare the simulated data to consolidated data of real epidemic dynamics.
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Introduction

Conventional epidemiology of infectious disease requires extensive collections of population, health and disease pattern data, as well as data related to environmental factors and social conditions. An epidemiologic study may focus on a particular region or a particular outbreak, or it may take as its theme the epidemiology of a condition across a wide area. The range and amount of data required will, therefore, vary depending on the type of study. Lack of data quality control, lack of definition about the content to be registered, storage heterogeneity and availability are some problems that must be solved to allow more precise and thorough studies in epidemiologic vigilance. In addition, public health data must be usually analyzed together with data from other fields, like data related to the environment, basic sanitation and the social services infra-structure. This set of databases has a variable degree of heterogeneity. As a consequence, it is cumbersome to work with these data in an integrated manner. Furthermore, analytical studies to identify risk factors related to epidemic development are eventually used by health agencies (Wagstaff, 2000, Axelson, 1999). These studies need several types of data, such as geo-referenced disease cases, space-temporal environmental data relevant to epidemic prevention and population data based on demographic and geographic information with territory expressiveness.

Considering this scenario, we present in this paper advances in the development of a large-scale system for space-time visualization, monitoring, modeling and analysis of epidemic data using a Grid platform (Foster, 2002). This system, dubbed IntegraEPI (Silva et al., 2007), is capable to provide the integration of heterogeneous databases related to epidemic analysis and to make available analytical and computational methods to increase the predicting capability of the public heath system, in order to optimize its activities and resources when dealing with epidemic outbreak and prevention. The main objectives of the IntegraEPI project are:

  • 1.

    To develop a distributed computational laboratory to simulate, test and foresee the epidemic outbreak of selected diseases in a city using a computational grid;

  • 2.

    To develop analytical methods and algorithms to visualize and analyze the available data in order to build detailed predictive models of the dynamics of epidemic outbreak; and

  • 3.

    To integrate through the grid large space-temporal databases containing public health system notification data, environmental data and urban infrastructure data.

This system, differently of what has been proposed, is integrated and consequently it enables the construction of detailed predictive models of the dynamics of disease spreading. It is worth noting that simulators implementing these models will be able to use actual, updated data (Gallo et al., 2007, Neto et al., 2006). These simulators will be executed into distributed virtual laboratories where the health agents would test the efficacy of different strategies before the real outbreak occurrence. The health authority will be able to decide about a set of possible actions that would be previously tested in a virtual population interacting in an urban infrastructure, considering its environmental factors, and finally compare the simulated data with the consolidated data of real epidemic dynamics (Silva et al., 2007).

The development of the system described in this introduction with all its integration levels represents a great challenge from the computational standpoint. Moreover, the integration of the set of related databases and space-temporal analysis methods together with a computational model based on contact patterns among individuals and real urban structures demands a high-performance computational environment. Therefore, a logical choice for a computational platform for executing this system is the Grid. We have then chosen to implement the system on a grid platform (Foster,2002) based on standards defined by the Open Grid Forum (OGF, 2008), the Globus Alliance (Globus, 2008) and the OASIS consortium (OASIS, 2008).

Key Terms in this Chapter

OASIS: Not-for-profit consortium that drives the development, convergence and adoption of open standards for the global information society.

Open Grid Forum (OGF): International community dedicated to accelerating grid adoption by providing an open forum for grid innovation and developing open standards for grid software interoperability.

World Health Organization (WHO): Coordinating authority for health within the United Nations system.

Web Service Resource Framework (WSRF): WSRF is a set of specifications for expressing the relationship between stateful resources and Web services. The specifications define specific message exchange formats and related XML definitions.

Globus Alliance: The Globus Alliance is a community of organizations and individuals responsible for the development of the Globus Toolkit.

Canonical Data Model (CDM): The Canonical Data Model is a technique utilized to standardize the data source models. The Canonical Data Model hides syntactical differences of multiple heterogeneous data sources and provides a common model for data querying.

Web Services: A software system designed to support interoperable machine to machine interaction over a network through the use of common standards.

OGSA-DAI: OGSA-DAI is a middleware, integrated in the Globus Toolkit version 4 distribution, which permits the data sources implemented with different technologies to be accessed on a grid.

Extensible Markup Language (XML): XML, describes a class of data objects called XML documents and partially describes the behavior of computer programs which process them. XML is an application profile or restricted form of SGML, the Standard Generalized Markup Language. By construction, XML documents are conforming SGML documents.

Globus Toolkit: Open source software toolkit used for building grids.

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