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 February 12 2018 by Marius Gilbert
A 2-year post-doc position is opened to work on spatial epidemics and phylogeographic models applied to Bluetongue. Description Several factors can contribute to the spread of animal diseases their relative effect can be difficult to disentangle. In this project, we aim to compare spatial epidemic models and spatial phylogeographic models in their capacity to quantify the effect of different factors on patterns of spread, using past bluetongue epidemics as study system. Read more...
On January 08 2018 by Marius Gilbert
Two papers were recently published on this topic from different perspectives. In the paper Intensifying poultry production systems and the emergence of avian influenza in China: a ‘One Health/Ecohealth’ epitome, we discussed different sets of ecological and epidemiological pressures that could have lead to the emergence of avian influenza in Asia. In the paper Could Changes in the Agricultural Landscape of Northeastern China Have Influenced the Long-Distance Transmission of Highly Pathogenic Avian Influenza H5Nx Viruses? Read more...
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...
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