Epidemic Intelligence Data and Vizualisation

Commissioned by the MOOD project to provide a visual analytics platform, the Montpellier Laboratory of Computer Science, Robotics and Microelectronics (LIRMM) developed the Epid Data Explorer (EDE) is a user-friendly platform that facilitates the monitoring and analysis of the progression of an epidemic over time and location by leveraging aggregated data. It enables users to perform cross-regional and cross-temporal analyses of epidemiological data, as well as covariate data (e.g. weather data).

The platform provides a comprehensive set of features that enable users to analyze data across various geographical regions and time frames. It offers a range of capabilities, including the ability to view data at different levels of temporal resolution, zoom in on specific regions for closer inspection, compare the behavior of
a particular region with the entire dataset, download selected data, and compare two indicators from different datasets on the same interface.

EDE supports common geocoding standards, such as NUTS (2021 revision) and ISO 3166 (2020 revision) codes, making it easy to integrate data from various sources. The platform accepts data in the form of a csv file, including formats used by organizations like ECDC and Our World in Data, as well as custom formats defined by the user.

EDE offers two options for use: through the Mood platform or on a local setup (on a personal computer or with personal data).

• The Mood platform can be accessed at: https://advanse.lirmm.net/EDE/epid-data-explorer/
• EDE can be downloaded and installed on a personal computer by accessing the code available at:
  The user interface for displaying the data:
  https://gite.lirmm.fr/advanse/EDE/epid-data-explorer;
  The data store that provides the data:
  https://gite.lirmm.fr/advanse/EDE/dataset

Please refer to the EpidDataExplorer_Explanations for further explanations. Its objective is to showcase the various features and capabilities of EDE and to provide instructions on how to install and set up the platform. The report is structured as follows: Chapter 1 provides a comprehensive overview of the key components of EDE, while Chapter 2 offers detailed instructions on how to install and configure EDE on a personal computer, including how to add new data to the platform. Chapter 3 describes how to create and manage a data store for EDE.

Contacts:

• Pascal Poncelet (ADVanced Analytics for data SciencE)
• Jérôme Azé
• Fati Chen
• Pierre Pompidor
• Vincent Raveneau
• Nancy Rodriguez
• Arnaud Sallaberry
• Laetitia Viau