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.
On September 29 2017 by Marius Gilbert
In this paper, published in Science today, we conducted a first global assessment of different intervention policies that could help limit the projected increase of antimicrobial use in food production. The paper is lead by Thomas Van Boeckel a former member of the lab, now at ETH Zurich, and was carried out in collaboration with researchers from FAO, Princeton university and CDDEP. The paper reports that worldwide antimicrobial consumption is expected to rise by a staggering 52% and reach 200,000 tonnes in 2030 barring any actions. Read more...
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...
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...
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.See more
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.See more
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.See more
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.See more