Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modeling (9/9)

levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference (8/9)

which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying (7/9)

As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this study, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), (6/9)

Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations (5/9)

and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling (4/9)

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, (3/9)

A study conducted at the Spatial Epidemiology Lab (SpELL, spell.ulb.be) of the @ulbruxelles.bsky.social, led by Fabiana Gámbaro, with also the contributions and help of Maylis Layan, @guybaele.bsky.social, and Bram Vrancken, as well as the support of the F.R.S.-FNRS (2/9)

A huge thank you to @kylaserres.bsky.social for having coordinated the logistical aspects of this edition, as well as to the FNRS and @ulbruxelles.bsky.social for their support

A great opportunity to present and discuss about current research projects in the respective teams and beyond, enhancing interdisciplinarity, and opening new collaboration opportunities

A study performed with Fabiana Gámbaro, @maudejacquot.bsky.social, @lequimelab.eu, @guybaele.bsky.social, @mariusgilbert.bsky.social, Oliver Pybus, @msuchard.bsky.social, and @lemeylab.bsky.social - 8/8

Continuous phylogeographic simulations and landscape phylogeographic analyses were all conducted using new functions implemented in the R package “seraphim” available at github.com/sdellicour/s..., which now also comes with new dedicated tutorials. - 7/8