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Since the start of the COVID-19 pandemic, an unprecedented number of genomic sequences of SARS-CoV-2 have been generated and shared with the scientific community. The unparalleled volume of available genetic data presents a unique opportunity to gain real-time insights into the virus transmission during the pandemic, but also a daunting computational hurdle if analyzed with gold-standard phylogeographic approaches. To tackle this practical limitation, we here describe and apply a rapid analytical pipeline to analyze the spatiotemporal dispersal history and dynamics of SARS-CoV-2 lineages. As a proof of concept, we focus on the Belgian epidemic, which has had one of the highest spatial densities of available SARS-CoV-2 genomes. Our pipeline has the potential to be quickly applied to other countries or regions, with key benefits in complementing epidemiological analyses in assessing the impact of intervention measures or their progressive easement., SCOPUS: ar.j, info:eu-repo/semantics/published

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown once again that coronavirus (CoV) in animals are potential sources for epidemics in humans. Porcine deltacoronavirus (PDCoV) is an emerging enteropathogen of swine with a worldwide distribution. Here, we implemented and described an approach to analyze the epidemiology of PDCoV following its emergence in the pig population. We performed an integrated analysis of full genome sequence data from 21 newly sequenced viruses, along with comprehensive epidemiological surveillance data collected globally over the last 15 years. We found four distinct phylogenetic lineages of PDCoV, which differ in their geographic circulation patterns. Interestingly, we identified more frequent intra- and interlineage recombination and higher virus genetic diversity in the Chinese lineages compared with the USA lineage where pigs are raised in different farming systems and ecological environments. Most recombination breakpoints are located in the ORF1ab gene rather than in genes encoding structural proteins. We also identified five amino acids under positive selection in the spike protein suggesting a role for adaptive evolution. According to structural mapping, three positively selected sites are located in the N-terminal domain of the S1 subunit, which is the most likely involved in binding to a carbohydrate receptor, whereas the other two are located in or near the fusion peptide of the S2 subunit and thus might affect membrane fusion. Finally, our phylogeographic investigations highlighted notable South-North transmission as well as frequent long-distance dispersal events in China that could implicate human-mediated transmission. Our findings provide new insights into the evolution and dispersal of PDCoV that contribute to our understanding of the critical factors involved in CoVs emergence., SCOPUS: ar.j, info:eu-repo/semantics/published

Computer simulations of genetic data are increasingly used to investigate the impact of complex historical scenarios on patterns of genetic variation. Yet, in most empirical studies, relatively large portions of species ranges are often treated as panmictic populations, ignoring the underlying spatial context. In some cases, however, a more accurate spatial model is required. We use a spatially explicit model of coalescence (easily constructed by overlaying a two-dimensional grid on maps displaying an estimate of past and current species ranges) to evaluate the potential of several summary statistics to differentiate three typical phylogeographic scenarios. We first explore the variation of each summary statistic within the boundaries of each phylogeographic scenario, and identify those that appear most promising for a comparison of historical scenarios and/or to infer historical parameters. We then combine a selected set of summary statistics in a single chi-square statistic and evaluate whether it can be used to differentiate past geographic fragmentation or range expansion from a simple scenario of isolation by distance. We also investigate the benefits of using a spatially explicit model by comparing its performance to alternative models that are less spatially explicit (lower geographic resolution). The results identify conditions in which each summary statistic is useful to infer the evolution of a species range, and allow us to validate our spatially explicit model of coalescence and our procedure to compare simulated and observed sequence data. We also provide a detailed description of the spatially explicit model of coalescence used, which is currently lacking.