## 3 Results
(**b**) The dynamics of the 12 most month-wise frequent lineages. (**c**) The dynamics of the 13–24 th most month-wise frequent lineages. Figure 3: The fastest spreading lineages. The data represent lineages with more than 100 Lithuanian sequences in GISAID. The data presented in (**a**,**b**) are based on spline interpolation of observed month fractions of all GISAID-deposited Lithuanian sequences. (**a**) Plot indicates the maximum first order derivative of the spline fitted to the month fractions of each lineage. (**b**) Plot indicates the time spans required to reach a certain fraction limit of all GISAID-deposited Lithuanian sequences (indicated by fill colour). (**c**) The plot represents calculated R0 values for the lineages based on the absolute number of corresponding lineages aggregated on a monthly basis. For some lineages, it was impossible to calculate R0 values, and they are not given. Figure 4: _Cont_. Figure 4: The most likely countries of origin for the most prevalent lineages based on the GTR model. (**a**) The Y axis depict frequency of transmission to Lithuania from the listed countries, as inferred from the time-scaled phylogenetic trees. The upper graph depicts the cumulative frequencies across different lineages. The small graphs below give details of the top four countries with the largest observed transmission frequencies to Lithuania. (**b**) The columns in the middle of the Sankey diagram depict lineages. The widths of the flows to the left side of these columns are proportional to the frequency of transmission to Lithuania, as inferred from the time-scaled phylogenetic trees. The width of the flows to the right side of the columns in the middle are proportional to the fraction of all GISAID-deposited Lithuanian sequences matching a particular lineage. Figure 5: Potential non-Lithuanian origin transmissions. The figure indicates the fraction of sequences included in detected transmission clusters for which the most recent common ancestor is a non-Lithuanian sequence. The results of correlational analysis between known emigration data and transmissions to Lithuania are given in Figure 6. As we see in the figure, phylogeographical estimates of foreign transmission intensities are directly proportional to the total emigration intensity from Lithuania within the last ten years, and corresponding Pearson and Spearman correlation estimates are statistically significant. It is worth noticing that, based on the residuals of the linear models, Denmark and, to a lesser extent, Norway stand out as having higher transmission intensity to Lithuania than expected. The higher-than-expected transmission intensity from Germany is evident despite the way we analysed phylogeographic transmissions. However, higher-than-expected transmissions from Norway are only evident using transmission cluster-based estimates. Correspondingly, India and Australia are notable for lower-than-expected transmission intensities (based on the TreeTime GTR model), along with Germany (based on transmission cluster estimates). We also checked if higher evaluated R0 values or initial spread dynamics could be related to higher transmission intensities from abroad to Lithuania. Pearson and Spearman correlations between frequency of transmissions to Lithuania and R0 were low and statistically insignificant. However, we detected a weak correlation between initial maximum growth rate and frequency of transmissions to Lithuania as inferred from the time-scaled phylogenetic trees. The data on both of these two aspects were available only for four lineages and are given in Figure 7. The P value for the Spearman correlation was 0.08 (R = 1). Figure 4.0 Relationship between total emigration rate (data up to 2020) and potential country of origin, as deduced from the phylogenetic analysis. Plots (**a,b**) depict data based on transmission frequencies inferred from time-scaled phylogenies. Plots (**c,d**) depict corresponding data inferred from transmission cluster analysis.