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ECLIPSE Project

The ECLIPSE Advanced Computational Platform 

EACP

The ECLIPSE Advanced Computational Platform(EACP), a distinctive cloud-based environment that supports the development and deployment of innovative tools for data-sharing, data analytics, visualization, and modeling for disease outbreaks, thereby enhancing public health emergency preparedness. This constitutes the primary outcome of S2 of Optional Component 2 (OC2). 

A cloud-based high performance computing platform that will enable advanced outbreak analytics and disease modeling. Focusing on accurate nowcasting and forecasting, advanced visualizations, intervention evaluations and unique roaming intelligence capabilities, the EACP aims to revolutionize the way we understand, track, and respond to disease outbreaks.

Our Solution

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Distributed RESTful Services-based Architecture

In this open-source software architecture, we will leverage Angular as the frontend framework for creating a dynamic and responsive user interface. Apache Tomcat will be used as the server to handle HTTP requests and serve the Angular application. Python will act as the backend server responsible for data modeling and analysis. A Hybrid Multi-Model Database combines the benefits of multiple database models, such as relational, document-oriented, graph, and others, into a single database system. 

Angular

Angular is a popular TypeScript-based frontend framework used for building single-page applications (SPAs). Angular 16 provides a wide range of tools and features for creating interactive user interfaces. It follows the component-based architecture and supports reactive programming through Observables.

Apache Tomcat

Apache Tomcat is an open-source web server and servlet container that provides a Java-based environment for running web applications. It will handle the HTTP requests coming from the clients and communicate with the backend server.

Python

Python is a versatile programming language widely used for data modeling and analysis tasks. It offers numerous libraries and frameworks, such as NumPy, pandas, and scikit-learn, that simplify data manipulation, statistical analysis, and machine learning tasks.

Hybrid Multi-Model Database

This approach offers several strengths and advantages, such as Versatility, Improved data integration, Enhanced performance, Simplified data access, Scalability and elasticity, Support for complex relationships and futureproofing.

  • PostgreSQL will be used as the relational database management system (RDBMS) to store and manage the application's data. It is a popular open-source database known for its reliability and performance.

  • MongoDB will be used as the document databases store data in a semi-structured format. Some of the strengths of this database are flexible, scalability and performance to handle large volumes of data and high traffic loads, High availability, High performance for read and write operations, Adaptability to languages and modern application development frameworks.

  • PostGIS provides a robust and feature-rich spatial database solution, making it suitable for applications that require advanced spatial analysis, integration with other systems, and reliable storage and retrieval of geospatial data.

  • Neo4J strengths lie in its native graph storage, powerful query language, high-performance graph operations, scalability, and extensive ecosystem. These features make it a powerful choice for applications that require complex relationship modeling, real-time graph analytics, and the ability to efficiently traverse and query highly interconnected data.

Background

The COVID-19 pandemic has highlighted the need for improved data handling, nowcasting, and forecasting techniques in managing public health crises. Existing public health systems face challenges due to fragmented data, lack of interoperability, and slow communication. Timely and accurate data is crucial for evidence-based decision-making, but privacy and security must also be considered. Trustworthiness with decision-makers is essential, along with the establishment of a comprehensive test bed for evaluating interventions. The pandemic has provided valuable lessons for future preparedness, emphasizing the importance of data-driven insights, international coordination, public engagement, and clear communication. Leveraging these learnings, the ECLIPSE Consortium aims to enhance collaboration, interoperability, data sharing, and predictive modeling for disease surveillance and intervention evaluation.

The consortium will create a connected community, an advanced computational platform, a comprehensive database, a training program, and a knowledge base to strengthen public health emergency preparedness.

The ECLIPSE initiative focuses on five key dimensions: interoperability and data integration, advanced analytics and predictive modeling, international and cross-sector collaboration, capacity building and training, and communication, coordination, and evaluation. Through these efforts, ECLIPSE seeks to revolutionize disease surveillance and intervention evaluation, ultimately improving public health responses and creating a healthier and safer future.

Governance

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Steering Committee

Comprising representatives from key partner organizations, including representatives of the CDC and CFA, research institutions, government agencies, and industry partners.

Executive Director

Tasked with the day-to-day management of the consortium, the Executive Director will coordinate efforts among partner organizations, manage project budget and resources, and oversee the implementation of different strategies supporting the execution of the ECLIPSE proposal.

Deputy Director

Collaborating with the Executive Director in managing the consortium, the Deputy Director will be responsible for overseeing operational aspects of the project, including coordinating the CWGs

Project Manager

Responsible for managing the overall project timeline, and risk assessment and management, the Project Manager will ensure the successful execution of the ECLIPSE project by closely monitoring progress.

Project Coordinator

An essential factor in the project team, is responsible for keeping all components and tasks in order, meticulously tracking the project's progress, managing the Gantt chart, and monitor timelines and resources to identify potential delays.

Collaborative Working Groups (CWG)

To address the five core dimensions of the project goals, five specialized CWGs will operate coordinately to address requirements covering Interoperability and Data (CWG1), Advanced Analytics and Predictive Modeling (CWG2), International and Cross-sector Collaboration (CWG3), Capacity Building and Training (CWG4), and Communication, Coordination and Evaluation (CWG5).

Operations Committee

The operations committee is responsible for planning, coordinating, and managing the day-to-day operational aspects of the project.

Advisory Board

An external Advisory Board will be constituted, with renowned members including researchers, public health professionals, data scientists, and representatives from relevant international organizations.

Our Approach

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