dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion This study has presented an attempt at drainage network representation for distributed hydrological modelling on urban catchments where the layout of the stormwater network is not available. A case study is presented on the Oued Fez catchment using two hydrological models of the ATHYS platform in event-based mode at hourly and 5-minute time steps. Original discharge data recorded at one streamflow gauge and rainfall data recorded at 1-4 rain gauges, depending on the study period, are used to calibrate and validate the selected models. Our findings show that the approach adopted to take into account the urban configuration of the Oued Fez catchment by modifying the drainage directions and by differentiating runoff production according to urbanisation improves the performance of the hydrological models.
1. 2) (Fig. 2a ). More recently, two related lineages have emerged (BA. 2 and BA. 3), both sharing many, but not all of the characteristic mutations of BA. 1 and both having many unique mutations of their own (Extended Data Fig. 4a, b ). While BA. 2 and BA. 3 are evolutionarily linked to BA. 1 in that they all branch off of the same B. 1. 1 node without obvious progenitors, the three sublineages evolved independently from one another along separate branches (Extended Data Fig. 4c, d ). The earliest specimens of BA. 2 and BA. 3 were both sampled after the earliest known BA. 1 in South Africa (8 November 2021 at the time of writing), on 17 November 2021 in Tshwane (Gauteng) and on 18 November 2021 in a neighbouring province (North West), respectively. We primarily focus here on the BA. 1 lineage, which is rapidly spreading in multiple countries around the world and is the lineage that was first officially designated as the Omicron VOC.
Using a phylodynamic model that accounts for variable genome sampling through time (birth-death skyline model (BDSKY)  19  ) yields a doubling time of BA. 1-assigned genomes from South Africa and Botswana (n = 552) of 3. 9 (95% HPD = 3. 5-4. 3) days with an effective reproduction number (R e ) of 2. 79 (95% HPD = 2. 60-2. 97) during the period from early November to early December. The BDSKY-estimated R e for the Gauteng province dataset is 3. 86 (95% HPD = 3. 43-4. 29) and 3. 61 (95% HPD = 3. 20-4. 02) for the 3-epoch and 4-epoch model, respectively (Extended Data Tables  4  and 5 ). Spatiotemporal phylogeographic analysis indicates that the BA. 1 variant spread from the Gauteng province of South Africa to seven of the eight other provinces and to two regions of Botswana from late October to late November 2021, and shows evidence of more recent transmission within and between other South African provinces (Fig.
11, 12 ). We therefore argue that the population level of protective immunity against Delta acquired during previous epidemic waves is high, and that partial immune evasion is a major driver for the observed dynamics of Omicron in South Africa. This notion is supported by recent findings that show an increased risk of SARS-CoV-2 reinfection associated with the emergence of Omicron in South Africa  47  and the initial results from neutralization assays  48. However, in addition to immune evasion, an increase or decrease in the transmissibility of Omicron compared with Delta cannot be ruled out. There are a number of limitations to this analysis. First, we estimated the growth advantage of Omicron based on early sequence data only. These data could be biased due to targeted sequencing of SGTF samples and stochastic effects (such as superspreading) in a low-incidence setting, which can lead to overestimates of the growth advantage and, consequently, of the increased transmissibility and immune evasion.
Conclusion Strong genomic surveillance systems in South Africa and Botswana enabled the identification of Omicron within a week of observing a resurgence in cases in Gauteng province. Immediate notification of the WHO and early designation as a VOC has stimulated global scientific efforts and has given other countries time to prepare their response. Omicron is now driving a fourth wave of the SARS-CoV-2 epidemic in southern Africa, and is spreading rapidly in several other countries. Genotypic and phenotypic data suggest that Omicron has the capacity for substantial evasion of neutralizing antibody responses, and modelling suggests that immune evasion could be a major driver of the observed transmission dynamics. Close monitoring of the spread of Omicron in countries outside southern Africa will be necessary to better understand its transmissibility and the capacity of this variant to evade post-infection and vaccine-elicited immunity.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-04411-y. HaploTypeCaller --min-pruning 0 argument was added to increase mutation calling sensitivity near sequencing gaps. For Nanopore, low-coverage regions with poor alignment quality (<85% variant homogeneity) near sequencing/amplicon ends were masked to be robust against primer drop-out experienced in the spike gene, and the sensitivity for detecting short inserts using a region-local global alignment of reads was increased. We also used the wf_artic (ARTIC SARS-CoV-2) pipeline as built using the Nextflow workflow framework  56.
In some instances, mutations were confirmed visually with. bam files using Geneious v. 2020. 1. 2 (Biomatters). The reference genome used throughout the assembly process was NC_045512. 2 (numbering equivalent to MN908947. 3). Raw reads from the Illumina COVIDSeq protocol were assembled using the Exatype NGS SARS-CoV-2 pipeline v. 1. 6. 1 (https://sars-cov-2. exatype. com/). This pipeline performs quality control on reads and then maps the reads to a reference using Examap. The reference genome used throughout the assembly process was NC_045512. 2 (accession number: MN908947. 3). Several of the initial Ion Torrent genomes contained a number of frameshifts, which caused unknown variant calls. Manual inspection revealed that these were probably sequencing errors resulting in mis-assembled regions (probably due to the known error profile of Ion Torrent sequencers)  57.
To resolve this, the raw reads from the IonTorrent platform were assembled using the SARSCoV2 RECoVERY (Reconstruction of Coronavirus Genomes & Rapid Analysis) pipeline implemented in the Galaxy instance ARIES (https://aries. iss. it). This pipeline fixed the observed frameshifts, confirming that they were artefacts of mis-assembly; this subsequently resolved the variant calls. The Exatype and RECoVERY pipelines each produce a consensus sequence for each sample. These consensus sequences were manually inspected and polished using Aliview v. 1. 27 (http://ormbunkar. se/aliview/). All of the sequences were deposited in GISAID (https://www. gisaid. org/)  15, 16  , and the GISAID accession identifiers are included in Supplementary Table  1. Raw reads for our sequences have also been deposited at the NCBI Sequence Read Archive (BioProject: PRJNA784038).
The number and position of the Omicron mutations has affected a number of primers and caused primer drop-outs across a range of sequencing protocols, especially within the RBD (https://primermonitor. neb. com/lineages). These primer drop-outs have resulted in a number of genomes missing stretches of the RBD, and can affect estimates of mutation prevalence and the determination of the true set of lineage-defining mutations. Given this,. bam files of all initial genomes were inspected using IG Viewer to confirm mutation calls where reference calls were suspected to be from low coverage at primer dropout sites  58. Lineage classification. We used the widespread dynamic lineage classification method from the Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN) software suite (https://github.
com/ hCoV-2019/pangolin)  17. This is aimed at identifying the most epidemiologically important lineages of SARS-CoV-2 at the time of analysis, enabling researchers to monitor the epidemic in a particular geographical region. For the Omicron variant described in this study, the corresponding PANGO lineage designation is BA. 1 (lineages v. 1. 2. 106). When first characterized, the lineage was designated B. 59, 60  , we constructed a dataset containing 221 sequences by randomly sampling five sequences from each month for each continent. No Oceania samples were available from July or August, and no South American sequences were available from July 2021 (ref. 61 ). These sequences were aligned together with a set of five high-quality BA. 1, six BA. 2 and one BA. 3 sequences (representing the known diversity of these clades on 5 December 2021) using MAFFT  62  with the default settings.
Whereas 3SEQ  37  and RDP5 (ref. 38 ) were used to analyse this dataset, a subsample of the 39 most divergent sequences from the dataset was analysed using the GARD recombination detection method  36. As none of these recombination detection methods normally use potentially informative deletion patterns, deletions in these alignments were recoded as nucleotide substitutions (one substitution per contiguous run of deleted nucleotides). Furthermore, to minimize multiple testing issues, BA. 1, BA. 2 and BA. 3 were tested for evidence of recombination among one another using individual sequences from each of these lineages (CERI-KRISP-K032254, EPI_ISL_7190366 and EPI_ISL_7526186, respectively) together with the Wuhan-Hu-1 sequence (which served as a reference point for rooting the four taxon phylogeny).
The default program settings were used throughout for recombination analyses, with the exception of RDP5 analysis, in which sequences were treated as linear and the window sizes for the SiScan and BootScan methods (two of the seven recombination detection methods applied in RDP5) were changed to 2,000 nucleotides. Selection analyses. We investigated the nature and extent of selective forces acting on BA. 1, BA. 2 and BA. 3 genes encoding individual protein products (respectively, a median of 110, 3 and 2. 5 unique BA. 1, BA. 2 and BA. 3 sequences per protein product encoding genome region). A subset of publicly available sequences (from the Virus Pathogen Database and Analysis Resource (ViPR); https://www. viprbrc. org/) was included as background sequences to contextualize selection signals detectable within the BA.
1, BA. 2 and BA. 3 lineages at the levels of complete protein product encoding regions, and individual codons (a median of ~100 sequences per protein coding region). Sequences were selected, quality-checked, aligned, and processed for BUSTED, RELAX, MEME, FADE, FEL and BGM selection analyses (all implemented in HyPhy v. 2. 5. 33)  63  using the automated RASCL pipeline as outlined previously  2, 9, 34. Structure modelling. We modelled the spike protein on the basis of the Protein Data Bank coordinate set 7A94, showing the first step of the spike protein trimer activation with one RBD domain in the up position, bound to the human ACE2 receptor  64. We used Pymol (The PyMOL Molecular Graphics System, v. 2. 2. 0) for visualization. Phylogenetic analysis. All sequences on GISAID  15, 16  designated Omicron (n = 686; date of access: 7 December 2021) were analysed against a globally representative reference set of SARS-CoV-2 genotypes (n = 12,609) spanning the entire genetic diversity observed since the start of the pandemic.
In brief, the reference set included: (1) all genomes from Africa assigned to PANGO lineage B. 1. 1 or any of its descendents, excluding those belonging to a VOC clade; (2) a representative subsampling of global data from the publicly maintained global build of Nexstrain (https://nextstrain. org/ncov/gisaid/global); and (3) the top thirty BLAST hits when querying GISAID BLAST for BA. 1 and BA. 2 sequences. This sampling scheme ensures that we analyse Omicron against the closest variants of the virus. Omicron and reference sequences were aligned using Nextalign  65. A maximum-likelihood tree topology was inferred in FastTree 66 under the following parameters: a General Time Reversible model of nucleotide substitution and a total of 100 bootstrap replicates  67. The resulting maximum-likelihood tree topology was transformed into a time-calibrated phylogeny in which branches along the tree were scaled in calendar time using TreeTime  68.
Time-calibrated BEAST analysis. To estimate a time-scale and growth rate from the genome sequencing data, BEAST (v. 1. 10. 4)  70, 71  was used to sample phylogenetic trees under an exponential growth coalescent model using a strict molecular clock. All BA. 1-assigned genomes from South Africa and Botswana (as of 11 December 2021) were included, with some lower coverage genomes removed, leaving a total of 553 genomes. The single South African BA. 2 genome (CERI-KRISP-K032307, EPI_ISL_6795834) was included to help to stabilize the root of the BA. 1 clade but the exponential growth coalescent model was applied only to BA. 1 (a constant population size coalescent was used for the rest of the tree). The rate of molecular evolution was estimated from the data. Two runs of 100 million iterations were compared to assess convergence, and then post-burnin samples were pooled to summarize parameter estimates.
Birth-death phylogenetic analysis. We analysed the full South Africa and Botswana dataset (n = 552, all BA. 1 assigned), and the reduced dataset containing only Gauteng province genomes (n = 277) using the serially sampled birth-death skyline (BDSKY) model  19  , implemented in BEAST2 (v. 2. 5. 2)  72. To allow for changes in genomic sampling intensity shortly after the discovery of the new lineage, we allowed the sampling proportion to vary with time while keeping all other models parameters constant over the study period. The choice of prior distributions for the model parameters is summarized in Extended Data Table  3. For each analysis, we used an HKY substitution model and a strict clock model with a fixed clock rate of 0. 75 × 10 -3 and 1. 1 × 10 -3 substitutions per site per year (s.
s. y. ) for the full South Africa and Botswana dataset, and Gauteng province-only dataset, respectively. To allow for comparisons with the exponential growth coalescent model, we also repeated the analyses with clock rates fixed at those estimated from the coalescent analyses (1. 2 × 10 -3 and 0. 3 × 10 -3 s. s. y. ). The mean duration of infectiousness was fixed at 10 days  73, 74. The effective reproduction number, R e , was assumed to be constant through time. The sampling proportion was assumed to be 0 before the collection time of the oldest sample and allowed to change at fixed times that were approximately equidistantly spaced between the oldest sample and the most recent sample. For Markov chain Monte Carlo (MCMC) analyses of the full South Africa and Botswana dataset, the maximum clade credibility tree from the exponential growth coalescent model was used as the starting tree.
We kept the tree topology fixed, estimating only internal node heights. To assess the robustness of our estimates of R e under different assumptions of temporal variations in the sampling proportion, we repeated the analyses with 3 instead of 4 equidistant change-time points. All of the other model parameters and priors were kept the same. For each analysis, we ran two independent chains of 100 million MCMC steps and sampled parameters every 10,000 steps. We used Tracer (v. 1. 7)  75 to evaluate MCMC convergence for each of the individual chains (effective sample size (ESS) > 200), which were then combined using LogCombiner to obtain the final posterior distribution after removing 10% of each chain as burn-in. The results were analysed using the bdskytools package in R (https://github. com/laduplessis/ bdskytools).
The resulting estimates for the time of the most recent common ancestor, exponential growth rate and doubling time are summarized in Extended Data Tables  4  and 5. With fixed clock rates of 0. 75 × 10 -3 and 1. 1 × 10 -3 s. s. y. for the full South Africa and Botswana dataset and Gauteng province-only dataset, respectively, the 3-epoch and 4-epoch BDSKY models resulted in similar estimates of the effective reproduction number, R e , for both datasets: 2. 74 (95% HPD = 2. 56-2. 92) and 2. 79 (95% HPD = 2. 60-2. 97) for the South Africa and Botswana dataset, and 3. 86 (95% HPD = 3. 43-4. 29) and 3. 61 (95% HPD = 3. 20-4. 02) for the Gauteng province-only dataset. Using a faster clock rate led to more recent common ancestors and higher estimates of the effective reproduction number and growth rate.
We examined the sensitivity of our estimates to different assumptions regarding the average duration of infectiousness by repeating the analysis of the South Africa and Botswana dataset with different fixed values of the becoming non-infectious rate: 52. 1 per year and 26. 1 per year, which translate to an infectious period of 7 and 14 days, respectively. These values were selected as plausible bounds based on the infectious period of asymptomatic cases and the time from symptom onset to two negative RT-PCR tests  74. The 4-epoch model was used with a fixed clock rate of 0. 75 × 10 -3 s. s. y. in these analyses. For each analysis, we ran three independent chains of 35 million MCMC steps and sampled parameters every 10,000 steps. We used Tracer (v. 1. 7)  75 to evaluate MCMC convergence for each of the individual chains (ESS > 200), which were then combined using LogCombiner to obtain the final posterior distribution after removing 10% of each chain as burn-in.
Phylogeographic analysis. MCMC analyses were run in duplicate in BEAST (v. 1. 10. 4)  70, 71  for a total of 100 million iterations sampling every 10,000 steps in the chain. Convergence of runs was assessed in Tracer (v. 1. 7. 1)  75  based on high effective sample sizes (>200) and good mixing in the chains. Maximum clade credibility trees for each run were summarized in TreeAnnotator after discarding the first 10% of the chain as burn in. Finally, the spatiotemporal dispersal of Omicron was mapped using the R package seraphim  76. Estimating transmission advantage. We analysed 805 SARS-CoV-2 sequences from Gauteng, South Africa, that were uploaded to GISAID with sample collection dates from 1 September to 1 December 2021 (ref. 15 ). We used a multinomial logistic regression model to estimate the growth advantage of Omicron compared with Delta at the time point at which the proportion of Omicron reached 50% (refs.
We estimated ϵ for different levels of τ and Ω. To propagate the uncertainty, we constructed 95% credible intervals (CIs) of the estimates from 10,000 parameter samples of ρ, D and R w. We assumed D to be normally distributed with a mean of 5. 2 days and a s. d. of 0. 8 days (ref. 80 ). We sampled from publicly available estimates of the daily R w based on confirmed cases during the early growth phase of Omicron in South Africa (1 October-31 October 2021; range = 0. 78-0. 85) (https://github. com/covid-19-Re)  81. All spike mutations seen in BA. 1 are listed at the top in red and coloured according to prevalence. Prevalence was calculated by number of mutation detections / total number of sequences. However, primer drop-outs have affected the RBD region spanning K417N, N440K and G446S, and so it is likely that these mutations may actually be more prevalent than indicated here.
38  )) to identify potential signals of recombination in sequence datasets containing the BA. 1, BA. 2 and BA. 3 sequences together with sequences representative of global SARS-CoV-2 genomic diversity. Potential evidence of a single recombination event involving BA. 1, BA. 2 and BA3 was identified by 3SEQ (P = 0. 03), GARD (delta c-AIC = 20) and RDP5 (GENECONV P = 0. 027; RDP P = 0. 006) within the NTD encoding region of spike. The most likely breakpoint locations for this recombination event were 21690 for the 5′ breakpoint (high likelihood interval between 15716 and 21761) and 22198 for the 3′ breakpoint (high likelihood interval between 22197 and 22774). However, these analyses could not reliably identify which of BA. 1, BA. 2 or BA. 3 was the recombinant. Phylogenetic analysis of the genome regions bounded by these breakpoints (genome coordinates 1-21689, 21690-22198 and 22199-29903) potentially supported (1) BA.
Acknowledgements We thank  L. de Gouveia ,  A. Buys ,  C. Fourie ,  N. Duma ,  M. Ndlovu  and other members of the  NICD Centre for Respiratory Diseases and Meningitis and Sequencing Core Facility ;  N. Govender ,  G. Ntshoe ,  A. Moipone Shonhiwa ,  D. Muganhiri ,  I. Matiea ,  E. Mathatha ,  F. Gavhi ,  T. Mashudu Lamola ,  M. Makhubele ,  M. Matjokotja ,  S. Mdleleni ,  M. Makhubela  from the national SARS-CoV-2 NICD surveillance team for NMCSS case data;  F. Mckenna ,  T. Graham Bell ,  N. Munava ,  S. Kwenda ,  M. Raza Bano  and  J. Khosa  from  NICD IT  for NMCSS case and test data (in particular, SGTF data); and the following people from the diagnostic laboratories for their assistance:  K. Reddy ,  L. Gounder  and  C. Naicker  from  NHLS Inkosi Albert Luthuli Central Hospital Laboratory ,  S.
Korsman  from the  NHLS Groote Schuur Laboratory , and  A. Enoch  at  NHLS Green Point Laboratory ; the staff at the global laboratories who generated and made public the SARS-CoV-2 sequences (through GISAID) used as reference dataset in this study (a complete list of individual contributors of sequences is provided in Supplementary Table 1). The research reported in this publication was supported by the  Strategic Health Innovation Partnerships Unit of the South African Medical Research Council , with funds received from the  South African Department of Science and Innovation. Sequencing activities at KRISP and CERI were supported in part by the  WHO , the  National Institutes of Health (NIH)  ( U01 AI151698 ) for the  United World Antivirus Research Network (UWARN) , and the  Rockefeller Foundation  (grants  2021 HTH 017  and  2020 HTH 062 ).
C. L. A. received funding from the  European Union 's  Horizon 2020 research and innovation programme , project  EpiPose  (no. 101003688 ). D. P. M. was funded by the  Wellcome Trust  ( 222574/Z/21/Z ). R. C. and A. R. acknowledge support from the  Wellcome Trust  (Collaborators Award  206298/Z/17/Z ,  ARTIC network ) and A. R. from the  European Research Council  (no. 725422 , ReservoirDOCS). V. H. was supported by the  Biotechnology and Biological Sciences Research Council (BBSRC)  (grant no. BB/M010996/1 ). A. E. Z. , J. T. , M. U. G. K. and O. G. P. acknowledge support from the  Oxford Martin School. M. U. G. K. acknowledges support from the  Rockefeller Foundation ,  Google. org, and the  European Horizon   2020 programme MOOD  (no. 874850 ). M. V. and the members of the  Zoonotic Arbo and Respiratory Virus Program , UP was funded through the ANDEMIA G7 Global Health Concept: contributions to improvement of International Health, COVID-19 funds through the  Robert Koch Institute.
The genomic sequencing at  UCT/NHLS  is funded from the  South African Medical Research Council and Department of Science and Innovation ; and by the  Wellcome Centre for Infectious Diseases Research in Africa  ( CIDRI-Africa ), which is supported by core funding from the  Wellcome Trust  ( 203135/Z/16/Z  and  222754 ). C. W. and J. B. are funded by the  EDCTP (RADIATES Consortium ;  RIA2020EF-3030 ). Sequencing activities at the NICD were supported by a conditional grant from the  South African National Department of Health  as part of the emergency COVID-19 response; a cooperative agreement between the  National Institute for Communicable Diseases of the National Health Laboratory Service  and the  United States Centers for Disease Control and Prevention  (no. 5U01IP001048-05-00 ); the  African Society of Laboratory Medicine (ASLM)  and  Africa Centers for Disease Control and Prevention  through a subaward from the  Bill and Melinda Gates Foundation  grant no.