Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2

Abstract

Superspreading events shaped the Coronavirus Disease 2019 (COVID-19) pandemic, and their rapid identification and containment are essential for disease control. Here we provide a national-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading during the first wave of infections in Austria, a country that played a major role in initial virus transmissions in Europe. Capitalizing on Austria’s well-developed epidemiological surveillance system, we identified major SARS-CoV-2 clusters during the first wave of infections and performed deep whole-genome sequencing of more than 500 virus samples. Phylogenetic-epidemiological analysis enabled the reconstruction of superspreading events and charts a map of tourism-related viral spread originating from Austria in spring 2020. Moreover, we exploited epidemiologically well-defined clusters to quantify SARS-CoV-2 mutational dynamics, including the observation of a low-frequency mutation that progressed to fixation within the infection chain. Time-resolved virus sequencing unveiled viral mutation dynamics within individuals with COVID-19, and epidemiologically validated infector-infectee pairs enabled us to determine an average transmission bottleneck size of 103 SARS-CoV-2 particles. In conclusion, this study illustrates the power of combining epidemiological analysis with deep viral genome sequencing to unravel the spread of SARS-CoV-2, and to gain fundamental insights into mutational dynamics and transmission properties.

Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity

“…Certain substitutions present a significantly higher normalized abundance, confirming recent findings on distinct cohorts (Simmonds, 2020; Di Giorgio et al, 2020; Popa et al, 2020). In particular, C>T substitutions are observed in ≈ 28% of all C nucleotides in the SARS-CoV-2 genome; G>T’s in ≈ 15% of all G’s, T>C’s in ≈ 7% of all T’s, and A>G’s in ≈ 5% of all A’s.…”

“…In particular, several studies on SARS-CoV-2 support the presence of intra-host genomic diversity in clinical samples and primary isolates (Ramazzotti et al, 2020; Shen et al, 2020; Wölfel et al, 2020; Capobianchi et al, 2020; Rose et al, 2020; Lu et al, 2020; Lythgoe et al, 2020; Seemann et al, 2020; Popa et al, 2020), whereas similar results were obtained on SARS-CoV (Xu et al, 2004), MERS (Park et al, 2016), EBOLA (Ni et al, 2016) and H1N1 influenza (Poon et al, 2016). We here present one of the the largest up-to-date studies on intra-host genomic diversity of SARS-CoV-2, based on a large dataset including 1133 high-quality samples for which raw sequencing data are available (NCBI BioProject: ).…”

Temporal dynamics of SARS-CoV-2 mutation accumulation within and across infected hosts

“…Finally, measurements of intrahost diversity must be accurate and account for several potential sources of error 22,23 . Although previous studies have described within-host variation of SARS-CoV-2 7,9,11,12,24–26 , few have addressed the sources of systematic errors and batch effects in variant identification. To assess the utility of SARS-CoV-2 intrahost diversity for transmission inference, we need a clearer understanding of its temporal variation throughout infection and the extent of convergent evolution across individuals.…”

Upgrade social distancing into "Airflow Inaccessible Distancing" code validated by E. coli DH5a spray viable colony counting simulation and the technical validation of COVID-19 Airborne Infection UV Quarantine Devices

“…In a pandemic world, people try to know how much concentration of SAR-VOR-2 viruses in the aerosol will induce infection. It is so challenging that till now no exact report is available; there is only one report claim 10 1 -10 3 viable viral particles [28] from estimation. Our E.coli spray simulation system has never used the SAR-VOR-2 viruses or synthesis DNA segments spray, however, E.coli viable colony counting does reveal the stronger correlation between human respiratory rhythm and viral infection concentration, which sustain the above lower limit of the estimation.…”