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Poster de conférence Année : 2023

MOnitoring Outbreak events for Disease surveillance in a data science context

Résumé

Climate change, animal and human mobility, growing populations, and urbanization increase the risk of emergence and spread of new pathogens. It is crucial to rapidly detect hazard emergence and assess the risk to public health through all the available sources of data. The MOOD project aims to develop innovative tools and services to answer these needs and monitor current and future infectious disease threats in the context of global, environmental, and climatic change. Through big data and disease modelling innovations, the MOOD project is addressing the challenges of cross-sectoral data sharing and valorization in a One Health framework based on multi-disciplinary collaborations for the animal, human, and environmental health. Co-creation of tools and services with human and veterinary public-health agencies, responsible for designing and implementing strategies to mitigate the identified risks, is at the core of MOOD innovation: the process started by a participatory users needs assessment, further defined with potential users during the development of tools through case studies. We are currently developing a platform to provide access to epidemic intelligence and disease surveillance practitioners to the MOOD outputs. It will have three main modules; a covariate access module where users can visualize and download relevant standardized covariates related to infectious disease emergence in support of risk assessment and modeling. The second is an epidemiological data visualization module where users can visualize and access data on current disease outbreaks, extracted from online media news using text mining, as well as their own collected data, on a GDPR compliant and secure local version of the tool. The last module will provide risk maps and other modelled outputs, aiming at highlighting areas suitable for the emergence of infectious diseases in animals and humans, to support improved disease detection, monitoring, and surveillance. In parallel, an evaluation of the influence of the level of co-creation on the uptake and effectiveness of MOOD tools and services is conducted through an integrated epidemiological and socio-anthropological approach. 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), which will perpetuate the work of the MOOD project.
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Dates et versions

halshs-04184801, version 1 (22-08-2023)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification - CC BY 4.0

Identifiants

  • HAL Id : halshs-04184801 , version 1

Citer

Pierrine Didier, Henok A Tegegne, Fanny Bouyer, Elena Arsevska, Timothée Dub, et al.. MOnitoring Outbreak events for Disease surveillance in a data science context. SVEPM - Society for Veterinary Epidemiology and Preventive Medicine, Mar 2023, Toulouse, France. 2023. ⟨halshs-04184801⟩
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