At the Spatial Epidemiology Lab (SpELL), we study 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 climatic, ecological or anthropogenic variables, and their integration into spatial and/or molecular approaches is used to predict the geographical distribution of risk and the environmental factors impacting the dispersal history and dynamic of pathogen spreads, which can contribute to better targetted prevention, surveillance and control measures. We also work toward the development, improvement and application of methods in spatial modelling of biological invasions, ecological niche modelling for risk mapping, and landscape phylogeography. The SpELL is also involved in the assembly of large-scale data sets on farm animals and in studies dedicated to the conservation of insect pollinators.
On April 16 2026 by Simon Dellicour
In March 2024, Brazil reported an unprecedented Oropouche fever outbreak, driven by the emergence of a novel reassortant lineage of the Oropouche virus expanding beyond the Amazon Basin. Oropouche virus (OROV) is an arthropod-borne virus first identified in 1955 in Oropouche, a village in Trinidad and Tobago. OROV typically causes a febrile illness with symptoms such as fever, headache, myalgia, arthralgia, photophobia, nausea, vomiting, and dizziness. In some cases, the illness can progress to severe neurological complications, including meningo-encephalitis. Read more...
On March 30 2026 by Diana Erazo
During the last decade, the Horn of Africa has experienced a worrying rise in urban malaria outbreaks, coinciding with the detection of Anopheles stephensi, an invasive malaria vector originally from Asia and well adapted to city environments. In this study, we investigated whether the recent spread of An. stephensi in the Horn of Africa could be attributed to climate change. To do so, we combined a database of geo-referenced occurrence records from across the species’ native and expanded range with ecological niche models based on boosted regression trees, and compared estimates obtained under observed historical climate conditions with those derived from a counterfactual climate baseline in which long-term climate trends were removed. Read more...
On March 24 2026 by Simon Dellicour
Ten years after the release and publication of its version 1.0, we are happy to communicate about the release of the version 2.0 of the toolbox “seraphim”, our R package for studying phylogenetically informed movements. This toolbox can for instance be used to investigate the impact of environmental factors on the dispersal history and dynamics of viral lineages, to estimate lineage dispersal statistics, to map continuous phylogeographic reconstructions, or to conduct continuous phylogeographic simulations. Read more...
Spatially-explicit phylogeographic analyses can be used to reconstruct the dispersal history of viral lineages. Over the last years, we started exploiting such phylogeographic reconstructions to investigate the impact on environmental factors on the dispersal dynamic of viral lineages (such as their diffusion velocity, dispersal position, and dispersal frequency). Furthermore, we also aim to use phylogeographic reconstruction to assess hypothetical intervention strategies in the context of viral epidemics.
See moreWe further develop and applies methodologies to conduct ecological niche modelling for the risk mapping of infectious diseases. Our research initially focused on avian influenza, with a particular emphasis on the role of agro-ecological factors on its emergence and persistence, but we have then also worked on other important livestock diseases such as bluetongue, bovine tuberculosis, and Nipah virus infections. More recently, we have conducted studies dedicated to the impact of climate and land-use changes on the distribution of pathogens of OneHealth importance such as the Lassa virus in Africa and the West Nile virus in Europe.
See moreInvading organisms spreading though an heterogeneous landscape are difficult to study using conventional statistical models. We aim to review existing methods, to develop new methodologies to study those type of data, and to compare all methods in their capacity to detect the influence of landscape heterogeneity on the pattern of spread. In particular, we have developed an analytical framework that allows testing the impact of continuous environmental layer as well as barriers on a wavefront progression.
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