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.


Variant-specific introduction and dispersal dynamics of SARS-CoV-2 in New York City – from Alpha to Omicron

On May 15 2023 by Simon Dellicour

Our new study on the introduction and dispersal dynamics of introduction of SARS-CoV-2 variants in New York City has been published in PLoS Pathogens. Since the latter part of 2020, SARS-CoV-2 evolution has been characterised by the emergence of viral variants associated with distinct biological characteristics. While the main research focus has centred on the ability of new variants to increase in frequency and impact the effective reproductive number of the virus, less attention has been placed on their relative ability to establish transmission chains and to spread through a geographic area. Read more...

A collaboration with the Grubaugh Lab: new study on Powassan virus published in PNAS

On April 11 2023 by Simon Dellicour

Powassan virus is an emerging tick-borne virus of concern for public health, but very little is known about its transmission patterns and ecology. In our recent study performed in collaboration with the Grubaugh Lab just published in PNAS, we expanded the genomic dataset by sequencing 279 Powassan viruses isolated from Ixodes scapularis ticks from the northeastern United States. Our phylogeographic reconstructions revealed that Powassan virus lineage II was likely introduced or emerged from a relict population in the Northeast between 1940-1975. Read more...

Our new study about the atypically pathogenic H3N1 avian influenza epidemic that occurred in 2019 in Belgium

On January 23 2023 by Simon Dellicour

The high economic impact and zoonotic potential of avian influenza call for detailed investigations of dispersal dynamics of epidemics. We integrated phylogeographic and epidemiologic analyses to investigate the dynamics of an H3N1 low pathogenic avian influenza epidemic that occurred in Belgium during 2019. Virus genomes from 104 clinical samples originating from 85% of affected farms were sequenced. A spatially-explicit phylogeographic analysis confirmed a dominating northeast to southwest dispersal direction and a long-distance dispersal event linked to direct live animal transportation between farms. 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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