West Nile Virus as a model for exotic pathogens transmitted by endemic vectors

West Nile virus (WNV) infection is a mosquito-borne zoonosis that is endemo-epidemic in Europe, affecting several countries in southern, eastern and western Europe. WNV incidence is increasing in Europe and its presence is expanding into new areas where it had never been observed before, becoming a global public health concern.

The virus is transmitted among host birds mainly via the bite of infected mosquitoes and incidentally, humans and other mammals may become infected. A number of abiotic (environmental) and biotic factors are known to determine its transmission dynamics.

Temperatures have been reported as one of the important environmental drivers influencing WNV transmission in suitable habitats in Europe as it affects either mosquito breeding success and the extrinsic incubation of the virus.

In face of climatic change, anomalous summer temperatures are considered among the factors affecting the incidence rate and the emergence of the virus into new areas, often associated with new cases in Europe.

Following the analysis of the needs expressed by Public Health and Veterinary Health practitioners from five different countries, MOOD is now working on the WNV case study, as a model for exotic pathogens transmitted by endemic vectors. Leading research experts, Public Health representatives (end-users), data and computer scientists and web developers are joining forces over the next two years to design and develop sustainable Epidemic Intelligence tools that will enhance preparedness and response to current and future WN outbreaks.

WN Tools

Generic tools for event-based surveillance

1a. PadiWeb + ProMED (tbc) connected to visualisation engine (EpiVis)

General tools for risk mapping

PadiWeb + ProMED (to be confirmed) connected to visualisation engine (EpiVis)

How to better identify relevant OneHealth determinants and determine risk thresholds based on data?
How to better understand environmental risk in terms of vector distribution, vector abundance, vector capacity of transmission and the lack of competence?
How to automatize the processing and analysis of environmental data (machine learning) for the risk assessment of vector-borne diseases?