Seroprevalence, Prevalence, and Genomic Surveillance: Monitoring the Initial Phases of the SARS-CoV-2 Pandemic in Betim, Brazil
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Abstract
The COVID-19 pandemic has created an unprecedented need for epidemiological monitoring using diverse strategies. We conducted a project combining prevalence, seroprevalence, and genomic surveillance approaches to describe the initial pandemic stages in Betim City, Brazil. We collected 3239 subjects in a population-based age-, sex- and neighborhood-stratified, household, prospective; cross-sectional study divided into three surveys 21 days apart sampling the same geographical area. In the first survey, overall prevalence (participants positive in serological or molecular tests) reached 0.46% (90% CI 0.12–0.80%), followed by 2.69% (90% CI 1.88–3.49%) in the second survey and 6.67% (90% CI 5.42–7.92%) in the third. The underreporting reached 11, 19.6, and 20.4 times in each survey. We observed increased odds to test positive in females compared to males (OR 1.88 95% CI 1.25–2.82), while the single best predictor for positivity was ageusia/anosmia (OR 8.12, 95% CI 4.72–13.98). Thirty-five SARS-CoV-2 genomes were sequenced, of which 18 were classified as lineage B.1.1.28, while 17 were B.1.1.33. Multiple independent viral introductions were observed. Integration of multiple epidemiological strategies was able to adequately describe COVID-19 dispersion in the city. Presented results have helped local government authorities to guide pandemic management.
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SciScore for 10.1101/2021.10.21.21265140: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The raw data generated were filtered by Trimmomatic v0.39 [9], which trimmed low-quality bases (Phred score < 30) and removed short reads (<50 nucleotides) as well as adapters and primer sequences. Trimmomaticsuggested: (Trimmomatic, RRID:SCR_011848)Reads were then mapped against the SARS-CoV-2 reference genome (accession number: NC_045512.2) with Bowtie2 [10]. Bowtie2suggested: (Bowtie 2, RRID:SCR_016368)The resulting BAM files were manipulated with SAMtools, BCFtools [11], and BEDtools [12] to generate consensus genome sequences. SAMtoolssuggested: (SAMTOOLS, RRID:SCR_002105)BEDtoolssugge…SciScore for 10.1101/2021.10.21.21265140: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The raw data generated were filtered by Trimmomatic v0.39 [9], which trimmed low-quality bases (Phred score < 30) and removed short reads (<50 nucleotides) as well as adapters and primer sequences. Trimmomaticsuggested: (Trimmomatic, RRID:SCR_011848)Reads were then mapped against the SARS-CoV-2 reference genome (accession number: NC_045512.2) with Bowtie2 [10]. Bowtie2suggested: (Bowtie 2, RRID:SCR_016368)The resulting BAM files were manipulated with SAMtools, BCFtools [11], and BEDtools [12] to generate consensus genome sequences. SAMtoolssuggested: (SAMTOOLS, RRID:SCR_002105)BEDtoolssuggested: (BEDTools, RRID:SCR_006646)These sequences were aligned with MAFFT v7.475[14], and a maximum likelihood tree was inferred on IQ-Tree 2 [15], under the GTR+F+I+G4 model [16], [17]. MAFFTsuggested: (MAFFT, RRID:SCR_011811)Maximum likelihood trees were inferred from these datasets, and their temporal signal was evaluated with tempest v1.5.3 [19]. tempestsuggested: (TempEst, RRID:SCR_017304)Time scaled phylogenies were then inferred from these datasets with BEAST v1.10.4 [20], using: (i) the HKY+I+G4 nucleotide substitution model [17], (ii) the strict molecular clock model, (iii) the non-parametric coalescent skygrid tree prior [21] and (iv) a symmetric discrete phylogeographic model [22]. BEASTsuggested: (BEAST, RRID:SCR_010228)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study presents some limitations. First, the household survey is less likely to sample severe cases, thus underestimating symptomatic Covid-19. Second, all clinical data were self-reported, which may lead to reporting bias [46]. Third, we could not sequence all PCR positive samples due to the low viral load and sequencing technology employed. Nevertheless, our study shows the potential to integrate different epidemiological inquiries (prevalence, seroprevalence, and genomic surveillance) to describe pandemic dispersion adequately. Moreover, our findings present original and relevant evidence that has helped local government authorities to guide pandemic management.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 21. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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