The research published here presents a new probabilistic model that enables us to estimate pathogen migration speeds far more accurately than pre-existing techniques. This advance has an impact in terms of public health and prevention, as the new model potentially enables us to predict geographical areas at high risk of being impacted by an epidemic.
A new model for the joint analysis of genetic sequences and their spatial coordinates. The study of correlations between physical and genetic distances between DNA sequences requires the development of techniques derived from the phylogenetic approach. The latter is coupled with probabilistic modeling of the random processes that characterize spatial diffusion. The phylogeographic approach is used in particular to monitor the evolution and spread of viruses during epidemics.
However, the models used to date were unable to accurately estimate the speed at which pathogens migrate during their evolution. In collaboration with IRD, the Catholic University of Louvain in Belgium and UCLA in the USA, we have proposed a new spatial diffusion model. Our results show that this model enables us to estimate diffusion rates more accurately than conventional approaches. Based on these results, the model has been successfully applied to the prediction of the spread of West Nile Virus (WNV) in the USA: from the analysis of viral sequences and their spatial coordinates, our model is able to predict the likely regions of circulation of viral strains over a time horizon of one year. This is one of the first applications in which a method for reconstructing past evolution is used to anticipate the future.
Contact : stephane.guindon@lirmm.fr
