tetano
Editor, Senior Moderator
J Infect Public Health
. 2022 Dec 14;16(2):171-181.
doi: 10.1016/j.jiph.2022.12.007. Online ahead of print.
Analysis of SARS-CoV-2 genomic surveillance data during the Delta and Omicron waves at a Saudi tertiary referral hospital
D Obeid[SUP] 1 [/SUP], A Al-Qahtani[SUP] 2 [/SUP], R Almaghrabi[SUP] 3 [/SUP], S Alghamdi[SUP] 4 [/SUP], M Alsanea[SUP] 5 [/SUP], B Alahideb[SUP] 5 [/SUP], S Almutairi[SUP] 4 [/SUP], F Alsuwairi[SUP] 5 [/SUP], M Al-Abdulkareem[SUP] 5 [/SUP], M Asiri[SUP] 5 [/SUP], A Alshukairi[SUP] 6 [/SUP], J Alkahtany[SUP] 4 [/SUP], S Altamimi[SUP] 7 [/SUP], M Mutabagani[SUP] 7 [/SUP], S Althawadi[SUP] 7 [/SUP], F Alanzi[SUP] 8 [/SUP], F Alhamlan[SUP] 9 [/SUP]
Affiliations
Abstract
Background: Studying the genomic evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may help determine outbreak clusters and virus transmission advantages to aid public health efforts during the pandemic. Thus, we tracked the evolution of SARS-CoV-2 by variant epidemiology, breakthrough infection, and patient characteristics as the virus spread during the Delta and Omicron waves. We also conducted phylogenetic analyses to assess modes of transmission.
Methods: Nasopharyngeal samples were collected from a cohort of 900 patients with positive polymerase chain reaction (PCR) test results confirming COVID-19 disease. Samples underwent real-time PCR detection using TaqPath assays. Sequencing was performed with Ion GeneStudio using the Ion AmpliSeq™ SARS-CoV-2 panel. Variant calling was performed with Torrent Suite™ on the Torrent Server. For phylogenetic analyses, the MAFFT tool was used for alignment and the maximum likelihood method with the IQ-TREE tool to build the phylogenetic tree. Data were analyzed using SAS statistical software. Analysis of variance or t tests were used to assess continuous variables, and χ[SUP]2[/SUP] tests were used to assess categorical variables. Univariate and multivariate logistic regression analyses were preformed to estimate odds ratios (ORs).
Results: The predominant variants in our cohort of 900 patients were non-variants of concern (11.1 %), followed by Alpha (4.1 %), Beta (5.6 %), Delta (21.2 %), and Omicron (58 %). The Delta wave had more male than female cases (112 vs. 78), whereas the Omicron wave had more female than male cases (311 vs. 208). The oldest patients (mean age, 43.4 years) were infected with non-variants of concern; the youngest (mean age, 33.7 years), with Omicron. Younger patients were mostly unvaccinated, whereas elderly patients were mostly vaccinated, a statistically significant difference. The highest risk for breakthrough infection by age was for patients aged 30-39 years (OR = 12.4, CI 95 %: 6.6-23.2), followed by patients aged 40-49 years (OR = 11.2, CI 95 %: 6.1-23.1) and then 20-29 years (OR = 8.2, CI 95 %: 4.4-15.4). Phylogenetic analyses suggested the interaction of multiple cases related to outbreaks for breakthrough infections, healthcare workers, and intensive care unit admission.
Conclusion: The findings of this study highlighted several major public health ramifications, including the distribution of variants over a wide range of demographic and clinical variables and by vaccination status.
Keywords: Breakthrough infections (BTI); COVID-19; Delta; Omicron; SARS-CoV-2 genomic surveillance; Variant of concern (VOC).
. 2022 Dec 14;16(2):171-181.
doi: 10.1016/j.jiph.2022.12.007. Online ahead of print.
Analysis of SARS-CoV-2 genomic surveillance data during the Delta and Omicron waves at a Saudi tertiary referral hospital
D Obeid[SUP] 1 [/SUP], A Al-Qahtani[SUP] 2 [/SUP], R Almaghrabi[SUP] 3 [/SUP], S Alghamdi[SUP] 4 [/SUP], M Alsanea[SUP] 5 [/SUP], B Alahideb[SUP] 5 [/SUP], S Almutairi[SUP] 4 [/SUP], F Alsuwairi[SUP] 5 [/SUP], M Al-Abdulkareem[SUP] 5 [/SUP], M Asiri[SUP] 5 [/SUP], A Alshukairi[SUP] 6 [/SUP], J Alkahtany[SUP] 4 [/SUP], S Altamimi[SUP] 7 [/SUP], M Mutabagani[SUP] 7 [/SUP], S Althawadi[SUP] 7 [/SUP], F Alanzi[SUP] 8 [/SUP], F Alhamlan[SUP] 9 [/SUP]
Affiliations
- PMID: 36543031
- DOI: 10.1016/j.jiph.2022.12.007
Abstract
Background: Studying the genomic evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may help determine outbreak clusters and virus transmission advantages to aid public health efforts during the pandemic. Thus, we tracked the evolution of SARS-CoV-2 by variant epidemiology, breakthrough infection, and patient characteristics as the virus spread during the Delta and Omicron waves. We also conducted phylogenetic analyses to assess modes of transmission.
Methods: Nasopharyngeal samples were collected from a cohort of 900 patients with positive polymerase chain reaction (PCR) test results confirming COVID-19 disease. Samples underwent real-time PCR detection using TaqPath assays. Sequencing was performed with Ion GeneStudio using the Ion AmpliSeq™ SARS-CoV-2 panel. Variant calling was performed with Torrent Suite™ on the Torrent Server. For phylogenetic analyses, the MAFFT tool was used for alignment and the maximum likelihood method with the IQ-TREE tool to build the phylogenetic tree. Data were analyzed using SAS statistical software. Analysis of variance or t tests were used to assess continuous variables, and χ[SUP]2[/SUP] tests were used to assess categorical variables. Univariate and multivariate logistic regression analyses were preformed to estimate odds ratios (ORs).
Results: The predominant variants in our cohort of 900 patients were non-variants of concern (11.1 %), followed by Alpha (4.1 %), Beta (5.6 %), Delta (21.2 %), and Omicron (58 %). The Delta wave had more male than female cases (112 vs. 78), whereas the Omicron wave had more female than male cases (311 vs. 208). The oldest patients (mean age, 43.4 years) were infected with non-variants of concern; the youngest (mean age, 33.7 years), with Omicron. Younger patients were mostly unvaccinated, whereas elderly patients were mostly vaccinated, a statistically significant difference. The highest risk for breakthrough infection by age was for patients aged 30-39 years (OR = 12.4, CI 95 %: 6.6-23.2), followed by patients aged 40-49 years (OR = 11.2, CI 95 %: 6.1-23.1) and then 20-29 years (OR = 8.2, CI 95 %: 4.4-15.4). Phylogenetic analyses suggested the interaction of multiple cases related to outbreaks for breakthrough infections, healthcare workers, and intensive care unit admission.
Conclusion: The findings of this study highlighted several major public health ramifications, including the distribution of variants over a wide range of demographic and clinical variables and by vaccination status.
Keywords: Breakthrough infections (BTI); COVID-19; Delta; Omicron; SARS-CoV-2 genomic surveillance; Variant of concern (VOC).