Spatial Epidemiology Lab

Université Libre de Bruxelles

Spatial epidemiology studies the effect of spatial factors on the emergence, spread and persistence of diseases and invasive species. The understanding of key spatial factors, such as environmental or anthropogenic variables, and their integration into spatial models can then be used to predict the geographical distribution of risk, which can contribute to better targetted prevention, surveillance and control measures.

More specifically, our spatial epidemiology lab is concerned with baseline works on the denominator, i.e. the number of hosts, which are key spatial variables used in most epidemiological models, and we actively work on the improvement of large-scale data sets on the distribution of human and livestock populations. A second focus of active research is the spatial epidemiology of avian influenza, a major disease of poultry with a strong zoonotic potential. The Spatial epidemiology lab. was founded in the academic year 2015-2016 and its researches were previously carried out under the Biological control and spatial ecology lab.


Spatial Statistics 2017 conference in Lancaster (UK)

On July 01 2017 by Marius Gilbert

Three members of the lab attended the Spatial Statistics 2017 conference in July, with talks on the larges-scale tranbsferability of H5Nx models (J. Artois), on farm distribution models (C. Chaiban) and on the latest developments of the gridded livestock of the world database (M. Gilbert). Read more...

H5N1 model helps predicting the spread of H5N8

On February 02 2017 by Marius Gilbert, Jean Artois, Madhur Dhingra & Catherine Linard

(Updated 2nd Feb. 2017) We evaluated the predictive capacity of our global H5N1 suitability model published a few month ago in e-life, and based on HPAI H5N1 and H5Nx records of years 2006-2015 in its capacity to predict the current wave of HPAI H5N8 across Europe. On February 1st, we extracted all the winter 2016:2017 H5N8 HPAI cases in domestic poultry (i.e. excluding wild bird cases) from the Empres-I database. Read more...

New papers in e-life on H5N1

On December 22 2016 by Marius Gilbert

A new paper has just been published in e-life mapping the global distribution of areas where HPAI H5N1 would have a high chance of sustained transmission upon introduction, as illustrated in the figure below. This was our first experience with e-life and we really enjoyed the quality and transparency of the peer-reviewing process, where referee comments are summarized and consolidated by the editorial team, and published, with their responses alonside the paper. Read more...

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Research topics

Livestock diseases

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.

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Mapping livestock

The geographical distribution of livestock (cattle, sheep, goat, pig, chicken, duck, buffaloes, camels) is a key driver of the distribution of diseases and has important environmental impacts at a global scale in terms of direct pollution through manure managment, greenhouse gaz emissions and contribution to antimicrobial resistance. Our work aim to better map the distribution of livestock production at a global scale, with some special emphasis on intensive livestock production and projections.

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Mapping human population

For many low-income countries of the World where disease burden is greatest, spatially detailed, contemporary census data on human population are missing. As partner of the Worldpop consortiumn, we are working on improving human population distribution maps in Africa and on urban expansion model that would allow us to project how the distribution of human population may change over time.

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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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