Additional file 3: Figure S9. of Quantifying predictors for the spatial diffusion of avian influenza virus in China

(A to F) Bayesian MCC phylogenies of 6 internal segments of 320 Chinese AIV sequences labelled with sequence names on tips and Bayesian posterior probability on nodes. (A) PB2; (B) PB1; (C) PA; (D) NP; (E) M; (F) NS. (ZIP 523 kb

Additional file 2: Figure S1. of Quantifying predictors for the spatial diffusion of avian influenza virus in China

Host distribution of 320 Chinese AIV sequences in Traditional Region, Economic Region, Economic Divided zone, and China Agro-Ecological Region types. Figure S2. Distributions of 320 Chinese AIV sequences in the sampled time, host order, subtype and sampled provinces. Figure S3. Influence of the sampling scheme on the phylogeographic reconstruction. Figure S4. PB1 tree mapping with region traits. Figure S5. PA tree mapping with region traits. Figure S6. NP tree mapping with region traits. Figure S7. M tree mapping with region traits. Figure S8. NS tree mapping with region traits. (PDF 864 kb

LSD Software

Link to LSD software. The initial commit is the LSD version used in all of the experiments described in our publication in Systematic Biology, plus examples, readme etc. We recommend NOT using this version, as LSD is regularly updated and improved. Use instead the version available from LSD web site: http://www.atgc-montpellier.fr/LSD/

Online Appendix of Least Square Dating article (LSD, To et al. Syst Biol 2015)

This Online Appendix contains the detailed description of our algorithms and their properties, and additional figures and tables

Additional file 1: of Using whole genome sequencing to investigate transmission in a multi-host system: bovine tuberculosis in New Zealand

Supplementary text 1.1: Filter Sensitivity Analysis. Supplementary text 1.2: Investigating Highly Distinct Isolates. Supplementary text 1.3: Temporal Signal. Supplementary text 1.4: Hierarchical Model Selection. Supplementary text 1.5: Influence of the Priors. Table S1. Results from investigating highly distinct isolates. Table S2. Results from hierarchical model selection. (DOCX 36 kb

Time-scaled MCC trees of WNV mapping with countries.

Trees were reconstructed using sequences of ns3 gene (a) and ns5 gene (b) mapping with countries (n = 14). (DOCX)</p

Results of drivers on viral dispersal velocity*.

BackgroundWest Nile virus (WNV) outbreaks in birds, humans, and livestock have occurred in multiple areas in Europe and have had a significant impact on animal and human health. The patterns of emergence and spread of WNV in Europe are very different from those in the US and understanding these are important for guiding preparedness activities.MethodsWe mapped the evolution and spread history of WNV in Europe by incorporating viral genome sequences and epidemiological data into phylodynamic models. Spatially explicit phylogeographic models were developed to explore the possible contribution of different drivers to viral dispersal direction and velocity. A “skygrid-GLM” approach was used to identify how changes in environments would predict viral genetic diversity variations over time.FindingsAmong the six lineages found in Europe, WNV-2a (a sub-lineage of WNV-2) has been predominant (accounting for 73% of all sequences obtained in Europe that have been shared in the public domain) and has spread to at least 14 countries. In the past two decades, WNV-2a has evolved into two major co-circulating clusters, both originating from Central Europe, but with distinct dynamic history and transmission patterns. WNV-2a spreads at a high dispersal velocity (88km/yr–215 km/yr) which is correlated to bird movements. Notably, amongst multiple drivers that could affect the spread of WNV, factors related to land use were found to strongly influence the spread of WNV. Specifically, the intensity of agricultural activities (defined by factors related to crops and livestock production, such as coverage of cropland, pasture, cultivated and managed vegetation, livestock density) were positively associated with both spread direction and velocity. In addition, WNV spread direction was associated with high coverage of wetlands and migratory bird flyways.ConclusionOur results suggest that—in addition to ecological conditions favouring bird- and mosquito- presence—agricultural land use may be a significant driver of WNV emergence and spread. Our study also identified significant gaps in data and the need to strengthen virological surveillance in countries of Central Europe from where WNV outbreaks are likely seeded. Enhanced monitoring for early detection of further dispersal could be targeted to areas with high agricultural activities and habitats of migratory birds.</div

Number of sequences per lineage in different countries and years.

Number of sequences per lineage in different countries and years.</p

Explanatory factors significantly attract WNV dispersal in Europe.

There are eleven factors (out of the total 37 factors being tested) that may attract WNV dispersal with strong statistical support (BF>20, as shown in S1 Table). The first four panels represent the percentage covered by each of the land cover types (Cropland, Urban land, land area changes from cropland to urban land, and Cultivated and Managed Vegetation) in 2015 in each grid cell. The visualizations and full descriptions of all factors are in the (S1 Fig and S2 Table). The unit of each predictor is shown after the predictor name above each panel. The European shapefile was created using the R package “rworldmap” (https://cran.r-project.org/web/packages/rworldmap/).</p

Supplementary Materials and Methods.

BackgroundWest Nile virus (WNV) outbreaks in birds, humans, and livestock have occurred in multiple areas in Europe and have had a significant impact on animal and human health. The patterns of emergence and spread of WNV in Europe are very different from those in the US and understanding these are important for guiding preparedness activities.MethodsWe mapped the evolution and spread history of WNV in Europe by incorporating viral genome sequences and epidemiological data into phylodynamic models. Spatially explicit phylogeographic models were developed to explore the possible contribution of different drivers to viral dispersal direction and velocity. A “skygrid-GLM” approach was used to identify how changes in environments would predict viral genetic diversity variations over time.FindingsAmong the six lineages found in Europe, WNV-2a (a sub-lineage of WNV-2) has been predominant (accounting for 73% of all sequences obtained in Europe that have been shared in the public domain) and has spread to at least 14 countries. In the past two decades, WNV-2a has evolved into two major co-circulating clusters, both originating from Central Europe, but with distinct dynamic history and transmission patterns. WNV-2a spreads at a high dispersal velocity (88km/yr–215 km/yr) which is correlated to bird movements. Notably, amongst multiple drivers that could affect the spread of WNV, factors related to land use were found to strongly influence the spread of WNV. Specifically, the intensity of agricultural activities (defined by factors related to crops and livestock production, such as coverage of cropland, pasture, cultivated and managed vegetation, livestock density) were positively associated with both spread direction and velocity. In addition, WNV spread direction was associated with high coverage of wetlands and migratory bird flyways.ConclusionOur results suggest that—in addition to ecological conditions favouring bird- and mosquito- presence—agricultural land use may be a significant driver of WNV emergence and spread. Our study also identified significant gaps in data and the need to strengthen virological surveillance in countries of Central Europe from where WNV outbreaks are likely seeded. Enhanced monitoring for early detection of further dispersal could be targeted to areas with high agricultural activities and habitats of migratory birds.</div