@sdellicour
F.R.S.-FNRS Research Associate at the University of Brussels (Spatial Epidemiology Lab - SpELL, https://spell.ulb.be/) and Visiting Professor at the University of Leuven (Evolutionary & Computational Virology lab, https://rega.kuleuven.be/cev/ecv)
Review: Phylogenetic insights into the transmission dynamics of arthropod-borne viruses by @viralverity.bsky.social, @sdellicour.bsky.social, Marta Giovanetti & @nathangrubaugh.bsky.social
go.nature.com/4iX9R6N
Free to read here: rdcu.be/e1t9M
#worldntdday
🦟📄 New preprint! Our first large-scale phylodynamic study of Jamestown Canyon virus (JCV) reveals how mosquito ecology shapes the evolution, persistence, and spread of this understudied arbovirus in North America.
Read it here! www.biorxiv.org/content/10.6...
I am very glad to share the preprint for our latest study on the 2024-2025 chikungunya virus outbreak on Réunion island, with primary data produced thanks to the massive efforts performed by Marie-C Jaffar and Etienne Frumence and analyses led by Simon Dellicour. www.medrxiv.org/content/10.6...
This study is the result of a collaborative effort involving Etienne Frumence, @raph-klitting.bsky.social, @kylaserres.bsky.social, Yucai Shao, Muriel Vincent, Mandev Gill, @msuchard.bsky.social, @lemeylab.bsky.social, Xavier de Lamballerie, and Marie-Christine Jaffar-Bandjee. 9/9
R scripts as well as input and output files related to the different phylodynamic analyses conducted in this study are available at github.com/sdellicour/c... (see also this page for a dynamic visualisation of the phylogeographic reconstruction). 8/9
While a short-term resurgence of viral transmission cannot be excluded, the impact of herd immunity constitutes an encouraging outcome that should at least contribute to limiting the spread of the virus in the upcoming seasons. 7/9
with frequent exchanges among distant residential areas. In addition, we show that the decrease in transmission rate leading to the end of the epidemic can, at least to a large extent, be attributed to the population immunity resulting from both the current and the 2005-2006 epidemic. 6/9
with viral transition events being more frequent from and toward more populated areas. While we find that dispersal events were on average more likely between geographically close locations, our analyses also reveal that the transmission chain was overall spatially intermixed, 5/9
Harnessing this genomic dataset, we used a set of phylodynamic and phylogeographic approaches to unravel the paths taken by the transmission chain and the external factors having impacted its dynamics on the island. Our analyses highlight a dispersal pattern in line with a gravity-model dynamic 4/9
In this study, we generated and analysed more than 3,000 time-stamped and geo-referenced near-full chikungunya virus genomes collected throughout the recent 2024-2025 outbreak, constituting one of the most comprehensive genomic datasets ever assembled for a non-COVID-19 viral epidemic. 3/9
It has been assessed that this new outbreak finds its origin in a single introduction event into the island, offering a unique opportunity to exploit viral genomic data to understand the epidemiological and dispersal dynamics of the introduced transmission chain. 2/9
Our latest study dedicated to the massive 2024-2025 chikungunya virus outbreak on Réunion island is now available as a preprint on medRxiv: doi.org/10.64898/202.... With more than 54,000 confirmed cases this is the second major chikungunya outbreak on the island, following the 1° one 20 yrs ago. 1/9
Review: Phylogenetic insights into the transmission dynamics of arthropod-borne viruses by @viralverity.bsky.social, @sdellicour.bsky.social, Marta Giovanetti & @nathangrubaugh.bsky.social
go.nature.com/4iX9R6N
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)
Check out our new study entitled "Navigating sampling bias in discrete phylogeographic analysis: assessing the performance of an adjusted Bayes factor" and now published in Molecular Biology & Evolution: academic.oup.com/mbe/article/... (1/9)
Gámbaro, @sdellicour.bsky.social et al. use simulations to evaluate an adjusted Bayes factor for discrete phylogeography that accounts for sampling bias, which lowers false positives and improves root inference.
🔗 doi.org/10.1093/molbev/msaf253
#evobio #molbio #phylogenetics
Our analysis of modelling practices, data use, and science-policy interactions during the COVID-19 pandemic is out on @eurosurveillance.org this week.
www.eurosurveillance.org/content/10.2...
Wonderful collaborative effort conducted in the context of mood-h2020.eu
Read the thread below 👇
In the COVID-19 Omicron surge, #KULeuven combined contact tracing with sequencing.
Only 1/3 of contacts shared the same strain 🤯, so most people misperceived where they got infected.
@thibaut-jonathan.bsky.social and team show how to assess contact tracing accuracy in the future!
#IDSky #EpiSky
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
Ongoing at the @ulbruxelles.bsky.social: the 4th edition of the Health Geography and Spatial Epidemiology workshop organised by the homonymous FNRS contact group (www.healthgeographygroup.eu)
The BEAST X logo - an octopus wrapping its noodley appendages round the letter X.
BEAST X v10.5.0 finally released – you can't just do beta releases forever. github.com/beast-dev/be...
Details and instructions for installing on the BEAST website: beast.community
Our study on the development and comparative performance of novel landscape phylogeography approaches has now been published in @pnas.org: lnkd.in/d5yuYwk6. A study led at the @ulbruxelles.bsky.social and @kuleuvenuniversity.bsky.social, and mainly conducted with the support of the F.R.S.-FNRS