Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference

Abstract

Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies.

Using phylogeographic approaches to analyse the dispersal history, velocity and direction of viral lineages — Application to rabies virus spread in Iran

“…Since one Q value was estimated per sampled tree, we thus obtained a distribution of Q values for each combination of environmental factor, k parameter value and path model. A variable can only be considered as potentially explanatory if both its distribution of regression coefficients and associated distribution of Q values are positive (Jacquot et al, ). In the final step, the statistical support of each Q distribution was evaluated against a null distribution generated by a randomization procedure and formalized as an approximated Bayes factor value (Dellicour et al, ).…”

Agricultural and geographic factors shaped the North American 2015 highly pathogenic avian influenza H5N2 outbreak

“…Recent incorporation of generalized linear models (GLM), a family of commonly used regression methods, into Bayesian phylogenetic frameworks have enabled investigation into the impact of ecological factors on the diffusion of viral pathogens among discrete geographic regions [16,17]. This is in contrast to continuous trait phylogeographic modeling, which can also model environmental impacts on viral spread, given location data is continuously distributed (i.e., latitude and longitude coordinates) [18][19][20]. Through such approaches, factors associated with HPAI movement within United States poultry industries might be identified, informing future control efforts.…”

Identification and Characterization of Bluetongue Virus Serotype 14 in Russia

“…The key factors driving the incursions, transmission, and overwintering of BTV in Europe remain poorly understood (42). In particular, the unexpected incursions into new territories, of which the arrival of BTV-8 in Northern Europe is a good example (43), require elucidation.…”