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Haplotypes over time Top panel: relative frequencies of major haplotypes in Denmark over time; bottom panel: total number of sequences over time. Both graphs are based on seven-day rolling averages.
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Background
The first cases of COVID-19 caused by the SARS-CoV-2 virus were reported in China in December 2019. The disease has since spread globally. Many countries have instated measures to slow the spread of the virus. Information about the spread of the virus in a country can inform the gradual reopening of a country and help to avoid a second w...
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The current pandemic of COVID-19 is caused by the SARS-CoV-2 virus for which many variants at the Single Nucleotide Polymorphism (SNP) level have now been identified. We show here that different allelic variants among 692 SARS-CoV-2 genome sequences display a statistically significant association with geographic origin (p < 0.000001) and COVID-19 c...
COVID-19 vaccine development is moving at a tremendous pace to fight the pandemic. Comorbidity came out as key contributing factor in COVID-19 severity and fatality. Although several approaches are taken in vaccine construction, main vaccine component inside our body will have viral spike (S) protein to induce immune response devoid of viral genome...
Background:
The spatiotemporal profiling of molecular transmission clusters (MTCs) using viral genomic data can effectively identify transmission networks in order to inform public health actions targeting SARS-CoV-2 spread.
Methods:
We used whole genome SARS-CoV-2 sequences derived from ten European regions belonging to eight countries to perfo...
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A novel coronavirus, SARS-CoV-2, has caused over 85 million cases and over 1.8 million deaths worldwide since it occurred twelve months ago in Wuhan, China. Here we conceptualized the time-series evolutionary and expansion dynamics of SARS-CoV-2 by taking a series of cross-sectional view of viral genomes from early outbreak in January in Wuhan to e...
The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which is believed to have originated in China towards the end of November 2019, has now spread across the globe, causing a pandemic in 192 countries. The World Health Organization has called it the SARS-CoV-2 pandemic. Rapid dissemination of the virus occurs mainly through the saliva...
Citations
... Indications of early cryptic circulation of SARS-CoV-2 have been reported from several countries in studies based on molecular epidemiology [4][5][6][7], wastewater surveillance [8][9][10] and mathematical modelling [11]. Concerning the travel-associated dispersal of the virus in Europe during the first pandemic wave, several reports indicate an important contribution from early and partly cryptic virus circulation in northern Italy and Austria [4,[11][12][13][14][15]. ...
... This might be explained by the earlier identification of Italy as a risk area compared with Austria. The Austrian Alps, and in particular the Ischgl ski resort in the Tyrol region, acted as a hub for transmission and dispersal of clade 20C [28] and were the source of introductions into Iceland, Denmark, Norway, Germany and Switzerland [13][14][15][28][29][30]. Likewise, Austria was the most common travel destination among Swedish cases infected with clade 20C and exposure abroad. ...
Background
Despite the unprecedented measures implemented globally in early 2020 to prevent the spread of SARS-CoV-2, Sweden, as many other countries, experienced a severe first wave during the COVID-19 pandemic.
Aim
We investigated the introduction and spread of SARS-CoV-2 into Sweden.
Methods
We analysed stored respiratory specimens (n = 1,979), sampled 7 February–2 April 2020, by PCR for SARS-CoV-2 and sequenced PCR-positive specimens. Sequences generated from newly detected cases and stored positive specimens February–June 2020 (n = 954) were combined with sequences (Sweden: n = 730; other countries: n = 129,913) retrieved from other sources for Nextstrain clade assignment and phylogenetic analyses.
Results
Twelve previously unrecognised SARS-CoV-2 cases were identified: the earliest was sampled on 3 March, 1 week before recognised community transmission. We showed an early influx of clades 20A and 20B from Italy (201/328, 61% of cases exposed abroad) and clades 19A and 20C from Austria (61/328, 19%). Clade 20C dominated the first wave (20C: 908/1,684, 54%; 20B: 438/1,684, 26%; 20A: 263/1,684, 16%), and 800 of 1,684 (48%) Swedish sequences formed a country-specific 20C cluster defined by a spike mutation (G24368T). At the regional level, the proportion of clade 20C sequences correlated with an earlier weighted mean date of COVID-19 deaths.
Conclusion
Community transmission in Sweden started when mitigation efforts still focused on preventing influx. This created a transmission advantage for clade 20C, likely introduced from ongoing cryptic spread in Austria. Therefore, pandemic preparedness should have a comprehensive approach, including capacity for large-scale diagnostics to allow early detection of travel-related cases and community transmission.
... Communities worldwide have implemented genomic surveillance by systematically sequencing the genomes of a percentage of local cases (Deng et al. 2020;Lu et al. 2020a;Meredith et al. 2020;Park et al. 2021). This has been important in tracing local transmission chains (Bluhm et al. 2020;Lam 2020), understanding the genetic makeup of viral populations within local communities (Gonzalez-Reiche et al. 2020;Franceschi et al. 2021;Thornlow et al. 2021a), uncovering the means by which viral lineages have been introduced to new areas (Castillo et al. 2020), and measuring the relative spread of specific variants (Skidmore et al. 2021;Umair et al. 2021). Phylogenetic approaches for better understanding the proximate evolutionary origins of the virus , as well as to identify recombination events (Jackson et al. 2021;Turakhia et al. 2021b) and instances of convergent evolution (Kalantar et al. 2020;Peng et al. 2021) have greatly informed our understanding of the virus. ...
Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 datasets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger datasets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established maximum likelihood implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar datasets with particularly dense sampling and short branch lengths.
... At least one ski resort known as Ischgl in Tyrol, Austria was recorded having an outbreak of SARS-CoV-2 in early March of 2020 51 , which aligns with the Bayesian estimations. Furthermore, viral strains from this resort have been similarly detected and might have seeded transmission chains in several other European countries [52][53][54] . Italy being identified as another major source is notable, as the second imported case of SARS-CoV-2 to Finland was by a returning traveller from Milan, Italy. ...
Background
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of infections and fatalities globally since its emergence in late 2019. The virus was first detected in Finland in January 2020, after which it rapidly spread among the populace in spring. However, compared to other European nations, Finland has had a low incidence of SARS-CoV-2. To gain insight into the origins and turnover of SARS-CoV-2 lineages circulating in Finland in 2020, we investigated the phylogeographic and -dynamic history of the virus.
Methods
The origins of SARS-CoV-2 introductions were inferred via Travel-aware Bayesian time-measured phylogeographic analyses. Sequences for the analyses included virus genomes belonging to the B.1 lineage and with the D614G mutation from countries of likely origin, which were determined utilizing Google mobility data. We collected all available sequences from spring and fall peaks to study lineage dynamics.
Results
We observed rapid turnover among Finnish lineages during this period. Clade 20C became the most prevalent among sequenced cases and was replaced by other strains in fall 2020. Bayesian phylogeographic reconstructions suggested 42 independent introductions into Finland during spring 2020, mainly from Italy, Austria, and Spain.
Conclusions
A single introduction from Spain might have seeded one-third of cases in Finland during spring in 2020. The investigations of the original introductions of SARS-CoV-2 to Finland during the early stages of the pandemic and of the subsequent lineage dynamics could be utilized to assess the role of transboundary movements and the effects of early intervention and public health measures.
... Alternatively, deliberate underreporting of high-risk exposures may have occurred despite pseudonymized data collection. Moreover, HCWs returning from early COVID-19 hotspots in late February 2020 [39,40], after the winter break in Southern Germany, may not have been aware of SARS-CoV-2 exposures during their vacation. ...
Purpose
To determine risk factors for coronavirus disease 2019 (COVID-19) in healthcare workers (HCWs), characterize symptoms, and evaluate preventive measures against SARS-CoV-2 spread in hospitals.
Methods
In a cross-sectional study conducted between May 27 and August 12, 2020, after the first wave of the COVID-19 pandemic, we obtained serological, epidemiological, occupational as well as COVID-19-related data at a quaternary care, multicenter hospital in Munich, Germany.
Results
7554 HCWs participated, 2.2% of whom tested positive for anti-SARS-CoV-2 antibodies. Multivariate analysis revealed increased COVID-19 risk for nurses (3.1% seropositivity, 95% CI 2.5–3.9%, p = 0.012), staff working on COVID-19 units (4.6% seropositivity, 95% CI 3.2–6.5%, p = 0.032), males (2.4% seropositivity, 95% CI 1.8–3.2%, p = 0.019), and HCWs reporting high-risk exposures to infected patients (5.5% seropositivity, 95% CI 4.0–7.5%, p = 0.0022) or outside of work (12.0% seropositivity, 95% CI 8.0–17.4%, p < 0.0001). Smoking was a protective factor (1.1% seropositivity, 95% CI 0.7–1.8% p = 0.00018) and the symptom taste disorder was strongly associated with COVID-19 (29.8% seropositivity, 95% CI 24.3–35.8%, p < 0.0001). An unbiased decision tree identified subgroups with different risk profiles. Working from home as a preventive measure did not protect against SARS-CoV-2 infection. A PCR-testing strategy focused on symptoms and high-risk exposures detected all larger COVID-19 outbreaks.
Conclusion
Awareness of the identified COVID-19 risk factors and successful surveillance strategies are key to protecting HCWs against SARS-CoV-2, especially in settings with limited vaccination capacities or reduced vaccine efficacy.
... Living space per person 1 per cent Median income 2 per cent Fraction of 1-person households 2 per cent Fraction of residents aged above 64 45 per cent modelling (Jay et al. 2020), by statistical evaluation (De Ridder et al. 2021b) or by phylogenetic clustering based on genomic sequencing data (Bluhm et al. 2020). Independent of model choice, the importance of socio-economic factors has been suggested previously (Jay et al. 2020;De Ridder et al. 2021a). ...
Transmission chains within small urban areas (accommodating ∼30 per cent of the European population) greatly contribute to case burden and economic impact during the ongoing coronavirus pandemic and should be a focus for preventive measures to achieve containment. Here, at very high spatio-temporal resolution, we analysed determinants of severe acute respiratory syndrome coro-navirus 2 (SARS-CoV-2) transmission in a European urban area, Basel-City (Switzerland). We combined detailed epidemiological, intra-city mobility and socioeconomic data sets with whole-genome sequencing during the first SARS-CoV-2 wave. For this, we succeeded in sequencing 44 per cent of all reported cases from Basel-City and performed phylogenetic clustering and compartmental modelling based on the dominating viral variant (B.1-C15324T; 60 per cent of cases) to identify drivers and patterns of transmission. Based on these results we simulated vaccination scenarios and corresponding healthcare system burden (intensive care unit (ICU) occupancy). Transmissions were driven by socioeconomically weaker and highly mobile population groups with mostly cryptic transmissions which lacked genetic and identifiable epidemiological links. Amongst more senior population transmission was clustered. Simulated vaccination scenarios assuming 60-90 per cent transmission reduction and 70-90 per cent reduction of severe cases showed that prioritising mobile, socioeconomically weaker populations for vaccination would effectively reduce case numbers. However, long-term ICU occupation would also be effectively reduced if senior population groups were prioritised, provided there were no changes in testing and prevention strategies. Reducing SARS-CoV-2 transmission through vaccination strongly depends on the efficacy of the deployed vaccine. A combined strategy of protecting risk groups by extensive testing coupled with vaccination
... Contact tracing data from Denmark, Sweden and Norway, provides evidence that the surge of cases in March 2020 was mostly related to traditional winter holiday destinations such as Austria (1150 cases) and Italy 4 . Similar evidence is provided from analyzing haplotypes in Denmark and Iceland 5,6 ). Notable as well, that during extensive contact tracing in Iceland in March/April 2020, only 2 of the 200 cases could be traced to foreigners/ tourists. ...
... Examples of such events include an international business conference in Boston during week 9 (February 26-27) 13 . From analyzing genome sequences, some cases of Covid-19 were likely transmitted from Denmark to Sweden in March 2020 6 . This is consistent with school holidays potentially being important since the breaks in Denmark are mostly in weeks 7/8 while the receiving areas are mostly in northern Sweden (week 10). ...
This paper investigates the role of large outbreaks on the persistence of Covid-19 over time. Using data from 650 European regions in 14 countries, I first show that winter school holidays in late February/early March 2020 (weeks 8, 9 and 10) led to large regional outbreaks of Covid-19 in the spring with the spread being 60% and up-to over 90% higher compared to regions with earlier school holidays. While the impact of these initial large outbreaks fades away over the summer months, it systematically reappears from the fall as regions with school holidays in weeks 8, 9 and 10 had 30–70% higher spread. This suggests that following a large outbreak, there is a strong element of underlying (latent) regional persistence of Covid-19. The strong degree of persistence highlights the long-term benefits of effective (initial) containment policies, as once a large outbreak has occurred, Covid-19 persists. This result emphasizes the need for vaccinations against Covid-19 in regions that have recently experienced large outbreaks but are well below herd-immunity, to avoid a new surge of cases.
... Communities worldwide have begun implementing genomic surveillance, the systematic genetic sequencing of a percentage of local cases (Deng et al. 2020;Lu et al. 2020a;Meredith et al. 2020;Park et al. 2021). This has been invaluable in tracing local transmission chains (Bluhm et al. 2020;Lam 2020), understanding the genetic makeup of viral populations within local communities (Gonzalez-Reiche et al. 2020;Franceschi et al. 2021; Thornlow et al. 2021a), uncovering the means by which viral lineages have been introduced to new areas (Castillo et al. 2020), and measuring the relative spread of specific variants (Skidmore et al. 2021;Umair et al. 2021). Phylogenetic approaches for better understanding the proximate evolutionary origins of the virus (Li et al.), as well as to identify recombination events (Jackson et al. 2021;Turakhia et al. 2021b) and instances of convergent evolution (Kalantar et al. 2020;Peng et al. 2021) have greatly informed our understanding of the virus. ...
Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 datasets do not fit this mould. There are currently over 5 million sequenced SARS-CoV-2 genomes in public databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between Likelihood and Parsimony approaches to phylogenetic inference. Maximum Likelihood (ML) methods are more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare. Therefore, it may be that approaches based on Maximum Parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger datasets. Here, we evaluate the performance of de novo and online phylogenetic approaches, and ML and MP frameworks, for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimizations produce more accurate SARS-CoV-2 phylogenies than do ML optimizations. Since MP is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo, we therefore propose that, in the context of comprehensive genomic epidemiology of SARS-CoV-2, MP online phylogenetics approaches should be favored.
... A similar event of interspecific transmission, unique to SARS-CoV-2, defined the zoonotic/anthropozoonotic character of this virus [9]; therefore, such events are significant for epidemiological analysis, as they determine the nature, scale, and duration of the infection [10]. However, the relevance of such studies is not accompanied by easily accessible information. ...
Insofar, due to the necessity to control human-to-human spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the overwhelming majority of the generated data on this virus was solely related to the genomic characteristics of strains infecting humans; conversely, this work aimed to recover and analyze the diversity of viral genomes from non-human sources. From a set of 3595 publicly available SARS-CoV-2 genome sequences, 128 lineages were identified in non-human hosts, the majority represented by the variants of concern Delta (n = 1105, 30.7%) and Alpha (n = 466, 12.9%), followed by B.1.1.298 lineage (n = 458, 12.7%). Environment, Neovison vison, Odocoileus virginianus and Felis catus were the non-human sources with the highest number of lineages (14, 12 and 10, respectively). Phylogenomic analyses showed viral clusters from environmental sources, N. vison, O. virginianus, Panthera tigris, and Panthera leo. These clusters were collectively related to human viruses as well as all other non-human sources that were heterogeneously distributed in the phylogenetic tree. Further, the genetic details of viral genomes from bats and pangolins were independently investigated owing to their high divergence, revealing five distinct clusters. Cluster 4 exclusively included bat-sourced genomes and the SARS-CoV-2 reference strain Wuhan-01. In summary, this study identified new genetic landmarks of SARS-CoV-2 evolution. We propose potential interspecies transmission routes of SARS-CoV-2 between animals and humans, which should be considered in order to establish better pathogen surveillance and containment strategies.
... Ski tourists partying in the Austrian alpine town of Ischgl caused a wide spread of the virus, especially to central and northern European countries. 122 Passengers on a number of cruise ships were also infected with COVID-19 early in the pandemic. As closed and dense environments where people gather in small areas, ships' spatial structures contributed to rapid spread of the disease on board. ...
... At the time of writing, more than 184 million people had been infected by SARS-CoV-2 [1][2][3][4] worldwide, and almost 4 million deaths related to COVID-19 had been registered (data from John Hopkins University), resulting in societies and health systems all over the world having to face challenges not known to humanity for many decades [5][6][7]. To manage the pandemic at both the national and international levels, first-line diagnosis, monitoring and care, and vaccination strategies are indispensable [8]. ...
In 2019, a novel coronavirus emerged in Wuhan in the province of Hubei, China. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread across the globe, causing the neoteric COVID-19 pandemic. SARS-CoV-2 is commonly transmitted by droplet infection and aerosols when coughing or sneezing, as well as high-risk exposures to infected individuals by face-to-face contact without protective gear. To date, a broad variety of techniques have emerged to assess and quantify the specific antibody response of a patient towards a SARS-CoV-2 infection. Here, we report the first comprehensive comparison of five different assay systems: Enzyme-Linked Immunosorbent Assay (ELISA), Chemiluminescence Immunoassay (CLIA), Electro-Chemiluminescence Immunoassay (ECLIA), and a new Particle-Enhanced Turbidimetric Immunoassay (PETIA) for SARS-CoV-2. Furthermore, we also evaluated the suitability of N-, S1- and RBD-antigens for quantifying the SARS-CoV-2 specific immune response. Linearity and precision, overall sensitivity and specificity of the assays, stability of samples, and cross-reactivity of general viral responses, as well as common coronaviruses, were assessed. Moreover, the reactivity of all tests to seroconversion and different sample matrices was quantified. All five assays showed good overall agreement, with 76% and 87% similarity for negative and positive samples, respectively. In conclusion, all evaluated methods showed a high consistency of results and suitability for the robust quantification of the SARS-CoV-2-derived immune response.
