Spatial Epidemiology Lab

Université Libre de Bruxelles

Spatial epidemiology studies the effect of spatial factors on the emergence, spread, persistence and evolution of diseases and invasive species. The understanding of key spatial factors, such as environmental or anthropogenic variables, and their integration into spatial models is used to predict the geographical distribution of risk, which can contribute to better targetted prevention, surveillance and control measures. We also work toward the improvement of methods in spatial modelling and landscape phylogeography, and of large-scale data sets on farm animals.


Delighted to welcome new researchers in our interdisciplinary team at the Spatial Epidemiology Lab

On October 14 2022 by Simon Dellicour

The Spatial Epidemiology Lab is delighted to welcome three new researchers in its interdisciplinary team: Fabiana Gámbaro whose post-doctoral project will be dedicated to application and methodological developments of phylodynamic approaches, Guillaume Ghisbain whose post-doctoral project will focus on investigating the dynamics and drivers of insect invasions at the European scale, as well as Jonathan Thibaut who just started a PhD project at the Laboratory of Clinical Microbiology of the KU Leuven and co-supervised at the SpELL. Read more...

Introducing the Fan-trap, an inexpensive, light and scalable insect trap

On October 13 2022 by Jean-Claude Grégoire

Monitoring is an important component of pest management, to prevent or mitigate outbreaks of native pests, and to check for quarantine organisms. Surveys often rely on trapping, especially when the target species respond to semiochemicals. Many traps are available for this purpose, but they are bulky in most cases, which raises transportation and deployment issues, and they are expensive, which limits the size and accuracy of any network. To overpass these difficulties, en-tomologists have used recycled material, such as modified plastic bottles, producing cheap and reliable traps but at the cost of recurrent handywork, not necessarily possible for all end-users (e. Read more...

Our new study about the evolution of the Lassa virus endemic area and population at risk

On September 23 2022 by Simon Dellicour

Lassa fever is a severe viral hemorrhagic fever caused by a zoonotic virus that repeatedly spills over to humans from its rodent reservoirs. It is currently not known how climate and land use changes could affect the endemic area of this virus, currently limited to parts of West Africa. By exploring the environmental data associated with virus occurrence using ecological niche modelling, we show how temperature, precipitation and the presence of pastures determine ecological suitability for virus circulation. Read more...

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Research topics

Landscape phylogeography

Spatially-explicit phylogeographic analyses can be used to introduce phylogenetic trees in a geographic context. Over the last years, we started exploiting such spatially-annotated trees to investigate the impact on environmental factors on the dispersal history and dynamic of viral lineages (dispersal velocity, dispersal direction and dispersal frequency). Furthermore, we also aim to use phylogeographic reconstruction to assess hypothetical intervention strategies in the context of viral epidemics.

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Livestock diseases and mapping

Our research mainly deal with the spatial epidemiology of avian influenza (AI) at different spatial scales, with particular emphasis on on the role of agro-ecological factors on the emergence, spread and persistence of AI viruses. Over the years, we have also been involved in research on other important livestock diseases such as bluetongue, bovine tuberculosis, foot and mouth disease, porcine reproductive and respiratory syndrome, and Nipah virus infections. In addition, we also have research projects to better map the distribution of livestock production at a global scale.

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Modelling geographical invasions

Invading organisms spreading though a heterogeneous landscape are difficult to study using conventional statistical models. We aim to develop new methodology to study those type of data, to review existing methods, and to compare all methods in their capacity to detect the influence of landscape heterogeneity on the pattern of spread.

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