153 research outputs found

    Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020

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    By October 2021, 230 million SARS-CoV-2 diagnoses have been reported. Yet, a considerable proportion of cases remains undetected. Here, we propose GInPipe, a method that rapidly reconstructs SARS-CoV-2 incidence profiles solely from publicly available, time-stamped viral genomes. We validate GInPipe against simulated outbreaks and elaborate phylodynamic analyses. Using available sequence data, we reconstruct incidence histories for Denmark, Scotland, Switzerland, and Victoria (Australia) and demonstrate, how to use the method to investigate the effects of changing testing policies on case ascertainment. Specifically, we find that under-reporting was highest during summer 2020 in Europe, coinciding with more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. In post-pandemic times, when diagnostic efforts are decreasing, GInPipe may facilitate the detection of hidden infection dynamics.Results - Method validation: in silico experiment. - Method validation: phylodynamics. - Reconstructed incidence histories. - Relative case detection rate. Discussion Method

    Molecular epidemiology of SARS-CoV-2: a regional to global perspective

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    Background After a year of the global SARS-CoV-2 pandemic, a highly dynamic genetic diversity is surfacing. Among nearly 1000 reported virus lineages, dominant lineages such as B.1.1.7 or B.1.351 attract media attention with questions regarding vaccine efficiency and transmission potential. In response to the pandemic, the Jena University Hospital began sequencing SARS-CoV-2 samples in Thuringia in early 2020.Methods Viral RNA was sequenced in tiled amplicons using Nanopore sequencing. Subsequently, bioinformatic workflows were used to process the generated data. As a genomic background, 9,642 representative SARS-CoV-2 genomes (1,917 of German origin) were extracted from more than 300.000 genomes.Results In a comprehensive bioinformatics analysis, we have set Thuringian isolates in the German, European and global context. In Thuringia, a largely rural German region without an international airport and a population density below the German average, we discovered many of the common “EU lineages”. German samples are scattered across eight major clades, and Thuringian samples occupy four of them.Conclusion The rapid emergence and spread of novel variants are of great concern as these lineages could transmit more efficiently, evade current vaccine efforts or undermine diagnostic test accuracy. To anticipate and mitigate these threats, a continuous molecular surveillance is essential.Key messagesBioinformatics analysis of 1,917, 4,251, and 3,474 SARS-CoV-2 genomes from Germany, the EU (except Germany), and non-EU, respectively, subsampled from more than 300,000 public genomes and placed in the context of Thuringian sequencesConstant antigenic drift for SARS-CoV-2 and no clear pattern or clustering is visible in Thuringia based on the current number of samplesCurrently over 100 described lineages are identified in Germany and only a subset (9) are detected in Thuringia so far, most likely due to genetic undersamplingFrom a national perspective, it is likely that high-frequency lineages, which are currently spreading throughout Europe, will eventually also reach ThuringiaSystematic and dense molecular surveillance via whole-genome sequencing is needed to detect concerning new lineages early, limit spread and adjust vaccines if necessaryCompeting Interest StatementThe authors have declared no competing interest.Funding StatementThe work is funded by the German Ministry of Education and Research (BMBF), grant number 01KX2021, and the Thuringian Region Government, grant number TZUZI82094.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:not applicableAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data is available on GISAID.Introduction Methods - Nanopore sequencing and genome reconstruction - Time tree creation Results - Most highly prevalent SARS-CoV-2 lineages in Germany detected in Thuringia - Genetic divergence and current lineage distribution Discussio

    The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology

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    YesBackground: Tracking recent transmission is a vital part of controlling widespread pathogens such as Mycobacterium tuberculosis. Multiple methods with specific performance characteristics exist for detecting recent transmission chains, usually by clustering strains based on genotype similarities. With such a large variety of methods available, informed selection of an appropriate approach for determining transmissions within a given setting/time period is difficult. Methods: This study combines whole genome sequence (WGS) data derived from 324 isolates collected 2005–2010 in Kinshasa, Democratic Republic of Congo (DRC), a high endemic setting, with phylodynamics to unveil the timing of transmission events posited by a variety of standard genotyping methods. Clustering data based on Spoligotyping, 24-loci MIRU-VNTR typing, WGS based SNP (Single Nucleotide Polymorphism) and core genome multi locus sequence typing (cgMLST) typing were evaluated. Findings: Our results suggest that clusters based on Spoligotyping could encompass transmission events that occurred almost 200 years prior to sampling while 24-loci-MIRU-VNTR often represented three decades of transmission. Instead, WGS based genotyping applying low SNP or cgMLST allele thresholds allows for determination of recent transmission events, e.g. in timespans of up to 10 years for a 5 SNP/allele cut-off. Interpretation: With the rapid uptake of WGS methods in surveillance and outbreak tracking, the findings obtained in this study can guide the selection of appropriate clustering methods for uncovering relevant transmission chains within a given time-period. For high resolution cluster analyses, WGS-SNP and cgMLST based analyses have similar clustering/timing characteristics even for data obtained from a high incidence setting.ERC grant [INTERRUPTB; no. 311725] to BdJ, FG and CJM; an ERC grant to TS [PhyPD; no. 335529]; an FWO PhD fellowship to PM [grant number 1141217N]; the Leibniz Science Campus EvolLUNG for MM and SN; the German Centre for Infection Research (DZIF) for TAK, MM, CU, PB and SN; a SNF SystemsX grant (TBX) to JP and TS and a Marie Heim-Vögtlin fellowship granted to DK by the Swiss National Science Foundation. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI

    Influenza A Virus Migration and Persistence in North American Wild Birds

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    Wild birds have been implicated in the emergence of human and livestock influenza. The successful prediction of viral spread and disease emergence, as well as formulation of preparedness plans have been hampered by a critical lack of knowledge of viral movements between different host populations. The patterns of viral spread and subsequent risk posed by wild bird viruses therefore remain unpredictable. Here we analyze genomic data, including 287 newly sequenced avian influenza A virus (AIV) samples isolated over a 34-year period of continuous systematic surveillance of North American migratory birds. We use a Bayesian statistical framework to test hypotheses of viral migration, population structure and patterns of genetic reassortment. Our results reveal that despite the high prevalence of Charadriiformes infected in Delaware Bay this host population does not appear to significantly contribute to the North American AIV diversity sampled in Anseriformes. In contrast, influenza viruses sampled from Anseriformes in Alberta are representative of the AIV diversity circulating in North American Anseriformes. While AIV may be restricted to specific migratory flyways over short time frames, our large-scale analysis showed that the long-term persistence of AIV was independent of bird flyways with migration between populations throughout North America. Analysis of long-term surveillance data provides vital insights to develop appropriately informed predictive models critical for pandemic preparedness and livestock protection. © 2013 Bahl et al

    Analysis of 3800-year-old Yersinia pestis genomes suggests Bronze Age origin for bubonic plague

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    © 2018 The Author(s). The origin of Yersinia pestis and the early stages of its evolution are fundamental subjects of investigation given its high virulence and mortality that resulted from past pandemics. Although the earliest evidence of Y. pestis infections in humans has been identified in Late Neolithic/Bronze Age Eurasia (LNBA 5000-3500y BP), these strains lack key genetic components required for flea adaptation, thus making their mode of transmission and disease presentation in humans unclear. Here, we reconstruct ancient Y. pestis genomes from individuals associated with the Late Bronze Age period (~3800 BP) in the Samara region of modern-day Russia. We show clear distinctions between our new strains and the LNBA lineage, and suggest that the full ability for flea-mediated transmission causing bubonic plague evolved more than 1000 years earlier than previously suggested. Finally, we propose that several Y. pestis lineages were established during the Bronze Age, some of which persist to the present day

    Stone Age Yersinia pestis genomes shed light on the early evolution, diversity, and ecology of plague

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    The bacterial pathogenYersinia pestisgave rise to devastating outbreaks throughouthuman history, and ancient DNA evidence has shown it afflicted human populations asfar back as the Neolithic.Y. pestisgenomes recovered from the Eurasian Late Neolithic/Early Bronze Age (LNBA) period have uncovered key evolutionary steps that led to itsemergence from aYersinia pseudotuberculosis-like progenitor; however, the number ofreconstructed LNBA genomes are too few to explore its diversity during this criticalperiod of development. Here, we present 17Y. pestisgenomes dating to 5,000 to 2,500y BP from a wide geographic expanse across Eurasia. This increased dataset enabled usto explore correlations between temporal, geographical, and genetic distance. Ourresults suggest a nonflea-adapted and potentially extinct single lineage that persistedover millennia without significant parallel diversification, accompanied by rapid dis-persal across continents throughout this period, a trend not observed in other pathogensfor which ancient genomes are available. A stepwise pattern of gene loss provides fur-ther clues on its early evolution and potential adaptation. We also discover the presenceof theflea-adapted form ofY. pestisin Bronze Age Iberia, previously only identified inin the Caucasus and the Volga regions, suggesting a much wider geographic spread ofthis form ofY. pestis. Together, these data reveal the dynamic nature of plague’s forma-tive years in terms of its early evolution and ecology

    Network Centrality of Metro Systems

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    Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no “winner takes all”) unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities

    Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2

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    Spatiotemporal bias in genome sampling can severely confound discrete trait phylogeographic inference. This has impeded our ability to accurately track the spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, despite the availability of unprecedented numbers of SARS-CoV-2 genomes. Here, we present an approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2. We demonstrate that including travel history data yields i) more realistic hypotheses of virus spread and ii) higher posterior predictive accuracy compared to including only sampling location. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts.status: publishe

    Urban road networks -- Spatial networks with universal geometric features? A case study on Germany's largest cities

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    Urban road networks have distinct geometric properties that are partially determined by their (quasi-) two-dimensional structure. In this work, we study these properties for 20 of the largest German cities. We find that the small-scale geometry of all examined road networks is extremely similar. The object-size distributions of road segments and the resulting cellular structures are characterised by heavy tails. As a specific feature, a large degree of rectangularity is observed in all networks, with link angle distributions approximately described by stretched exponential functions. We present a rigorous statistical analysis of the main geometric characteristics and discuss their mutual interrelationships. Our results demonstrate the fundamental importance of cost-efficiency constraints for in time evolution of urban road networks.Comment: 16 pages; 8 figure
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