Pilot 2.4 | EYWA - EarlY WArning System for Mosquito-Borne Diseases

Pilot 2.4 | EYWA - EarlY WArning System for Mosquito-Borne Diseases

More than 80% of the global population lives in areas at risk of at least one major Vector-Borne Disease (VBD), with more than 700.000 deaths at a global scale (WHO, 2020). Mosquitoes are the protagonists of these vectors, transmitting pathogens to living beings with the most important being the Mosquito-Borne Diseases (MBDs) in Europe, namely West Nile Fever linked to Culex mosquitoes, Malaria linked to Anopheles mosquitoes and Chikungunya, Dengue and Zika linked to Aedes mosquitoes.

There is a constantly increasing need to innovate on how the continuous threat of MBDs are confronted, treated but most of all foreseen. This gave birth to the idea of EYWA, an integrated and contemporary EarlY Warning System (EWS) for MBD, which utilizes state-of-the-art AI/ML technologies and furthermore assimilates big EO data and geo-spatial information, embodying a complete, adaptable (scalable, and replicable) and operational European EWS. EYWA offers operational and pre-operational services for MBD outbreak (TRL >7 up to 9) in five countries (France, Germany, Greece, Italy, Serbia).

Under the e-shape pilot EYWA will seek to further augment the database of entomological data from non-European territories and evolve the suite of predictive models to include non-European areas where the climate conditions are very different to those found in Europe. This will help make the model predictions even more robust in the face of different inputs. Furthermore, there will be an assessment of the combined accuracy gain of the mosquito abundance model MAMOTH with the dynamic epidemiological model MIMESIS for the West Nile virus (WNV) risk.

EYWA banner winner long

/ Objectives
  • To augment the database of pilot sites/regions with the inclusion of non-European territories from Côte d'Ivoire and Thailand. Ingest data from those new regions, and adapt and train and evaluate the outcomes of the existing epidemiological and entomological models of EYWA, and ensure their transferability to these new climatic zones and socio-economic standards characterizing these regions/countries.
  • Assess the benefit and accuracy gain of combining the predictive power of the data-driven mosquito abundance model MAMOTH with the dynamic epidemiological model MIMESIS for the West Nile virus (WNV) risk.
  • Augment the database of stakeholders and enlarge the network of beneficiaries of EYWA to sustain and address the SDGs targets at global level.
  • Create new possibilities to penetrate to the global market by addressing priorities and making EYWA visible across various sectors in the Health and outside the Health sector and establish links with prominent International Financial Institutions, Banks, Insurance and Reinsurance companies and Trust Funds.
  • Further support, with developed standards, the operations of European Health Emergency and Response Authority (HERA) and other EC (JRC, DG-ECHO) and ECDC services.
/ Partners
  • National Observatory of Athens, Greece
  • Laboratory of Atmospheric Physics, University of Patras, Greece
  • Bernhard Nocht Institute for Tropical Medicine, Germany
/ Key Users

All relevant communities that are involved in the control of Vector Borne Diseases:

  • Public health authorities
  • vector control companies
  • citizen scientists
  • researchers
/ Key Datasets
  • Earth Observation Data:
  • Sentinel-2
  • Landsat 7
  • Landsat 8
  • MODIS
  • IMERG
  • ERA5

Entomological data:

Mosquito abundance data as collected in-situ by partners

Epidemiological data:

Pathogen data that are collected in-situ

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/ ID Card
Expected outcome of the pilot

The final outcome of the pilot is two-fold. The first expected outcome is an enrichment of the EYWA project database with data from non-European sites, which will also be used to adapt, train and evaluate the epidemiological and entomological models. A second outcome is the investigation of the synergetic usage of the entomological and epidemiological models.

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