MOnitoring Outbreaks for Disease surveillance in a data science context

With climate change, animal and human mobility, growing populations and urbanization, there is now an increased risk of the emergence and accelerated the global spread of new pathogens. It is crucial that the emergence of a pathogen can be rapidly detected and assessed for the risk it poses to public health through all all the available sources of data. 

With the funds provided by the European Union’s Horizon 2020 ‘Research and innovation programme ( grant agreement No 874850), the MOOD project aims at taking advantage of data mining, analysis and visualization of health, environmental and other data to enhance the utility of event-based surveillance (EBS). Ultimately, MOOD is supporting the work of European and global public and veterinary health agencies and surveillance practitioner by providing existing monitoring platforms with novel features, methodological and practical support adapted to their needs.

A platform to enhance disease emergence and risk surveillance

MODULE 1:

Data & covariates access

A one-stop “shop” for the visualization and download of relevant standardized covariates relative to the MOOD model diseases and, more generally, to infectious disease emergence in support of risk assessment and modeling

MODULE 2

Event-based Surveillance data (EBS)

A visualization tool with the possibility to download data on disease outbreaks, extracted from online media news using text mining with Padi-web, an online media monitoring tool.

MODULE 3

Disease risk mapping

This module provides risk maps and other modelled outputs, aiming at highlighting areas suitable for the occurrence of (mainly) specific zoonoses in animals and humans, to support improved disease detection, monitoring and surveillance.

Data & covariates access

Many factors contribute to the (re-)emergence of infectious disease threats and to the development of antimicrobial resistance, altering the epidemiology and spread of diseases in a changing global environment . They increase the potential for human-to-human transmission, strengthening the epidemic/pandemic potential of pathogens, including those transmitted by arthropod vectors such as ticks or mosquitoes.The MOOD platform acquires, processes and integrates covariate data that drive disease distributions, which are standardised in terms of resolution, spatio-temporal extent, measurement units, and geospatial projection, to fit the requirements for the modelling activities. 

The MOOD Dataverse is a digital platform for preserving, managing and disseminating research data. Its use is intended for scientists and their partners as part of their research, expertise and training activities in partnership.

The platform is based on an open source web application  Dataverse, supported by Harvard University. The data is stored and secured within CIRAD’s IT infrastructure.

Dataverse is based on standards allowing the exchange of metadata and their indexing by search engines. A digital identifier DOI (Digital Object Identifier) is assigned to all data with mention of the authors, serving as a unique reference to find and cite the data.

Learn more

Contact:
William Wint – E.R.G.O. 

ERGO

MOOD TBE data dashboard preview

This tool allows to explore metadata about environmental and ecological covariates adopted in literature, with respect to tick-
borne encephalitis.
Click here for a demo.

The dashboard is based on the results of a literature review performed on articles published between 2000 and 2021, focusing on European countries.
We included primary research studies, modeling studies proposing quantitative analysis using explanatory variables (e.g. variables related to environment, vector and hosts ecology) and data collections with abstract and full-text document available in English. Studies with no data or with duplicated data were excluded (patents, editorials, letters, modelling studies with no data). We also excluded records with no denominator, no identified reference population, full-text document unavailable, low quality (or quality not assessable), and
referring to data older than 2000 or gathered outside Europe.

The visualizations include:

  •  The covariates category (environmental, host- and vector-related) and the specific variables adopted in published literature, ranked by the number of articles they
    appear in.
  •  The geographical distribution of selected articles.

 A summary table with the following features:

  •  First author, year of publication
  • Covariates explored in the study, with their values (when available),
    short description, and notes.
  • Response variable, i.e. the focus, of the study.
  • Type of analysis used in the study.
  •  Covariates data sources.

The report can be filtered by topic of interest, the type of analysis and type of covariates.
Graphs and tables can be explored by clicking on them and can be exported in .csv format.

Contact: Fondazione Edmund Mach, Applied Ecology Unit
Francesca Dagostin – francesca.dagostin@fmach.it

Event-based Surveillance data (EBS)

Epidemic intelligence integrates two components: indicator-based (e.g. official disease reports), and event-based surveillance (EBS) which looks at reports, stories, rumours, and other information about health events that could be a serious risk to public health. Because they are based on published reports which take time to produce and lack geographical precision, EBS tools may miss the first signals of the (re)-emergence of infectious diseases, as they may lack timeliness and spatial resolution. 

PADI-web (Platform for Automated extraction of Disease Information from the web) is an automated biosurveillance system dedicated to the monitoring of online news sources for the detection of animal health infectious events.
PADI-web automatically collects news with customized queries, classifies them and extracts epidemiological information (diseases, dates, symptoms, hosts and locations).

Watch this video to learn more!



Ask for a trial

A limited version is available with free access for research or web monitoring. To get access to advanced functionalities and extended data, please do not hesitate to contact padi-web@cirad.fr

Contacts:

Mathieu Roche (CIRAD) – text mining
Pascal Poncelet (LIRMM) – visualization
Elena Arservska (CIRAD) – epidemiology

Disease risk mapping

The integration of environmental, climatic, socioeconomic and demographic information, including human and animal mobility data, with the disease inputs can enhance the predictions of spatio-temporal, dynamic distributions that drive risk and spread assessment, and also decode the role of the environment, in infectious disease emergence.

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, an epidemiological data visualization platform that aims to better understand the spatial and temporal evolutions associated with epidemics.

Datasets can be visualized on two different maps with several features to facilitate exploration. Under each window, a drop-down list allows you to select both the data sets to be analyzed and also the desired variables. A timeline can be used to select the dates that will be displayed on the maps. Tooltips allow one to explore the temporal evolution of the selected variable for a given map area.

Visit website  How does it work?

Contacts:
Pascal Poncelet (ADVanced Analytics for data SciencE)
Laetitia Viau
Nancy Rodriguez
Pierre Pompidor

LIRMM logo

MOOD CASE STUDIES

Modeling seven diseases

The MOOD case studies establish a close collaborative space where MOOD researchers and end-users discuss together the development of Epidemic Intelligence (EI) tools for a routine use.

The disease-specific module (3) provides users with risk maps and other modelled outputs, aiming at highlighting areas suitable for the occurrence of (mainly) HPAI, WNV and TBE in animals and humans, to support improved disease detection, monitoring and surveillance.

Are you working on one or more diseases? Get involved!

A MOOD foundation to ensure the sustainability of the platform

The long term sustainability and societal impact of MOOD will be achieved through the creation of the MOOD Epi-Platform International Non-Profit Association (INPA).

The MOOD Epi-Platform Objectives

  1. Promote, maintain and further develop the standardised “MOOD Epi-Platform” that hosts the epidemiological e-tools developed by the MOOD project;
  2. Encourage the development, hosting and maintenance of additional state-of-the-art epidemiological e-tools and services, by its members, after the MOOD project is finished;
  3. Standardise and host relevant state-of-the-art epidemiological e-tools developed by third parties that wish to become a member of the INPA;
  4. Provide capacity building to support and promote the use of the tools offered by the MOOD Epi-Platform INPA. 

About MOOD

The MOOD project aims to develop innovative tools and services within one open-access platform for the early detection, assessment, and monitoring of current and potential infectious disease threats in Europe in a context of global change including climate change.

The MOOD innovations will increase the operational abilities of epidemic intelligence systems to face new disease threats, including emerging diseases of known or unknown origin, and antimicrobial resistance pathogens.

MOOD's targets

Human Health

Public Health
Human Health
Human-Disease Surveillance Practitioners

Animal Health
Veterinarians
Animal-Disease Surveillance Practitioners

One Health
Health international organizations, national agencies, Epidemic intelligence practitioners

FROM THE BLOG

Latest developments

PROJECT PROGRESS

Milestones & Deliverables

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Deliverable 7.10

This deliverable summarizes the main collaboration points between the MOOD and VEO project consortia for the second year of the MOOD project.

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