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 χ2 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: COVID-19, Variant of concern (VOC), Delta, Omicron, SARS-CoV-2 genomic surveillance, Breakthrough infections (BTI)
I. ntroduction
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The continued evolution of SARS-CoV-2, especially in regions with high levels of vaccination, highlights the importance of genomic surveillance. The ancestral virus strain emerged in Wuhan, China, and spread globally [1]. As of September 28, 2022, the total number of cases of COVID-19 infection worldwide exceeded 615 million, and the overall death toll was 6.5 million people [2]. Genetic changes in RNA viruses are common, even predicted, especially at the spike and receptor binding domain regions [3]. Those changes in SARS-CoV-2 may provide a natural selection advantage for variants that affect virus transmissibility, disease severity, immune escape, and therapeutic escape [4]. Since the pandemic started, the virus has been mutating slowly but steadily, with many variant sets classified as lineages.
The World Health Organization has been monitoring and assessing the emerged lineages, classifying them according to their risk at a global public health level. The classified lineages are considered variants of interest or variants of concern (VOCs) so that their monitoring and research will be prioritized to build the best response to the pandemic [5]. The main lineages (VOCs) showing the biggest global threats include the Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529) variants. Most of those lineages started as a single community transmission that went on to compete with the extant dominant subtype. The Omicron variant, which has been the dominating lineage in 2022, has five classified subtypes, starting with BA.1, which emerged in South Africa during the 2021–2022 winter.
Vaccination-based immunity is the most effective public health weapon against infectious diseases. Remarkably, during the COVID-19 pandemic, several vaccine types, including an RNA, a subunit, and a viral vector vaccine, were developed and tested within a year, preventing the death of 19.8–31.3 million people worldwide [6], [7]. The global challenge requiring more attention from public health authorities today is breakthrough infections (BTIs), which are increasing in countries with good vaccination coverage and vaccine accessibility. Inequality in COVID-19 vaccination accessibility due to limited resources and income is predicted to introduce immune escape variants [8].
According to the US Centers for Disease Control and Prevention, the definition of a BTI is infection with the virus that causes COVID-19 following vaccination with either a primary series or a primary series plus a booster [9]. These infections are a substantial concern in developed countries although many studies have shown lower disease severity, viral load, and death in this population than in the unvaccinated population [10], [11], [12]. The BTI rate was high when the Omicron variant first emerged because a two-dose vaccine elicited poor neutralization [13]. However, the addition of a booster vaccination was sufficient to obtain strong neutralization, which led to many health officials recommending it as a primary dose, especially in high-risk groups. The Omicron variant harbors 34 mutations in the spike gene, accounting for the virus's efficiency in infecting more cells than the other variants [14].
The highest numbers of BTIs have been found among people who are older or who have comorbidities regardless of their vaccination status [15], [16]. Another high-risk group is people who are immunocompromised. Chronic infections in such patients have driven significantly more SARS-CoV-2 genetic diversity, including the generation of mutations commonly found in VOCs [17]. Individuals who are unvaccinated, elderly, immunocompromised, or have comorbidities are most vulnerable to severe COVID-19 disease and have the highest potential to enable the emergence of new variants that may evolve to escape immunity.
The genetic diversity of SARS-CoV-2 in Saudi Arabia has important global ramifications because the country hosts millions of Muslim pilgrims annually. In Saudi Arabia, the total number of COVID-19 cases has exceeded 816,000, with 9300 deaths [2]. To date, 25.3 million people (72.6 % of the population) in Saudi Arabia were fully vaccinated [2]. The genetic diversity of SARS-CoV-2 evolved during the implementation of public health actions [18]. In Saudi Arabia, the first cases were detected in February 2020, and the first appearance of the Alpha and Beta VOCs was in early 2021 [19].
Studying the genomic evolution of SARS-CoV-2 at the level of community transmission may identify outbreak clusters, transmission advantages, and high-risk groups. Examining the evolution of SARS-CoV-2 at a single location may identify host factors and clusters with considerable effects on viral transmissibility. In this study, we aimed to tracked the evolution of SARS-CoV-2 during the Delta and Omicron waves at a tertiary referral hospital in Riyadh, Saudi Arabia. This hospital offers primary and highly specialized inpatient and outpatient medical care for immunocompromised patients in Saudi Arabia, as it has advanced treatments for oncology, organ transplantation, and cardiovascular disease. Our objectives in this study were to (1) track variant epidemiology by clinical variables, (2) evaluate vaccination status and dose by patient demographic and clinical characteristics, (3) identify risk factors for BTIs and intensive care unit (ICU) admission, and (4) conduct phylogenetic and cluster formation analyses for outbreaks by BTI, ICU admission, and vaccination status.
Methods
Collection of samples and clinical and demographic data
Samples were collected from 900 patients visiting the hospital with polymerase chain reaction (PCR)-confirmed SARS-CoV-2 by using a nasopharyngeal swab. The positive samples were sent to us from the Molecular Virology Section of the Pathology and Laboratory Medicine Department at King Faisal Specialist Hospital and Research Centre. Electronic medical records containing deidentified patient data were obtained from the Infection Control and Hospital Epidemiology Department. Variables assessed included demographic characteristics, vaccination history, comorbidities, symptoms, laboratory reports, and ICU admission. The inclusion criteria included all samples received from April 2021 to January 2022 that passed quality control metrics of sample integrity to be processed and analyzed by next-generation sequencing. We excluded all samples with poor quality control metrics.
Sample preparation
Nasopharyngeal samples (200 µL) underwent total nucleic acid RNA extraction using the MagMAX™ Viral/Pathogen II (MVP II) Nucleic Acid Isolation Kit. The RNA integrity was assessed using a Nanodrop system, with an accepted 260/280 ratio of approximately 2. Real-time PCR was performed targeting N (nucleocapsid), ORF1ab (open reading frame of 1ab), and S (spike) genes using TaqPath™ COVID-19 CE-IVD RT-PCR Kits by following the manufacturer's instructions. Real-time PCR was conducted using a 7500 Fast Real-Time PCR system and software (Applied Biosystems, California, USA). All samples positive for SARS-CoV-2 were converted to cDNA using SuperScript™ IV VILO™ Master Mix.
Sequencing and bioinformatics analysis
We conducted next-generation sequencing using an Ion GeneStudio S5 System (Thermo Fisher Scientific, USA). The cDNAs were amplified using the Ion AmpliSeq™ SARS-CoV-2 Insight Research Assay by following the manufacturer's instructions. Amplified products were ligated with unique barcode adapters using the Ion Xpress Barcode Adapters 1-16 kit and purified with Agencourt AMPure XP Reagent (1.5 ×; Beckman Coulter, USA). Constructed libraries were normalized to 33 pM using nuclease-free water, and up to 16 libraries were equally pooled. Pooled libraries were used as a template input for emulsion PCR and enrichment of template-positive particles using the Ion Chef automated system with the Ion 510 & Ion 520 & Ion 530 Kit-Chef kit according to the manufacturer's instructions. Enriched template-positive ion sphere particles were loaded onto an Ion 530 chip, and sequencing was performed with an Ion GeneStudio using Ion S5 sequencer. The obtained data were primarily processed (base calling and quality, alignment, assembly, and variant calling) with Torrent Suite™ software on the Torrent Server, version 5.12. De novo assembly of the contigs was performed using the assembly Trinity plugin (v1.2.1), and consensus sequences of each sample were generated using the IRMA plugin (v1.2.1). Variant call files were analyzed using the COVID-19 SnpEff plugin to identify and annotate variants with public and private databases.
Phylogenetic analysis
FASTA file names that we used were annotated to reflect patient information (sample identification, variant, month of infection, vaccinated or unvaccinated, healthcare worker [HCW] or not, and ICU admission or regular case). Sequences were then aligned against the reference (NC_045512.2) using the MAFFT (v 7.490) tool and 6merpair method to create a consensus alignment file [20]. For phylogenetic analyses, we used the maximum likelihood method with the IQ-TREE tool (v2.2.0-beta COVID-edition) [21]. We used the ModelFinder tool (within IQ-TREE) to select the best model for our samples, which was determined to be the TIM2 + F + I nucleotide substitution model [22]. To visualize the phylogenetic trees, we used FigTree software (v1.4.4) (http://tree.bio.ed.ac.uk/software/figtree/). All generated sequences were submitted to the GISAID database.
Ethics approval and consent
The Research Advisory Council (Clinical Research Committee) at King Faisal Specialist Hospital and Research Centre approved this study (RAC #220 0009) and granted a waiver for the requirement of obtaining informed consent owing to the use of deidentified samples.
Statistical analysis
All collected data were stored and analyzed using SAS, version 9.4, and Prism, version 9.0 (GraphPad). Inferential and descriptive statistics were conducted to study clinical variables. Analysis of variance or t tests were used to assess continuous variables, and χ2 tests were used to assess categorical variables. Univariate and multivariate logistic regression analyses were preformed to estimate the odds ratios (ORs) for BTIs and for ICU admission by patient clinical and demographic characteristics, and a global χ2 p-value is presented for each variable. All reported P-values were two-tailed and were considered to be statistically significant at< 0.05.
Results
Patient demographic and clinical characteristics
The mean (SD) age of 900 SARS-CoV-2–infected patients who contributed samples to this study was 36.6 (15.8) years; the youngest patient was 4 months of age, and the oldest patient was 102 years. Most patients were female (54.5 %), Saudi (65.3 %), and hospital employees (59 %). Only 17 patients were infected prior to travel. Most patients (75 %) received ≥ 1 dose of any available COVID-19 vaccine, including BNT162b2 (49.5 %), ChAdOx1 (31.2 %), and a mix of vaccines and other types (19.3 %). Most BTI cases were detected after the second vaccine dose (50.5 %), followed by after the first dose (45.6 %) and after a booster (3.9 %). Regarding comorbidities, 49.9 % of patients had a previous clinical burden, 9.2 % had hypertension, and 8.2 % had diabetes mellitus. A total of 21 patients (2.7 %) were admitted to the ICU. Using whole genome sequencing, we detected four lineages clustered into five groups: Alpha (B.1.1.7) (4.1 %), Beta (B.1.351) (5.6 %), Delta (B.1.617.2) (21.2 %), and Omicron (B.1.1.529) (58 %) variants, and non-VOCs (11.1 %). No Gamma lineage cases were detected in this cohort.
Detected variants and lineages by patient demographic and clinical characteristics
The first aim of this study was to track variant epidemiology by clinical variables. To that end, sample variants were called using the classification documentation of the World Health Organization (https://www.who.int/activities/tracking-SARS-CoV-2-variants) [5]. Fig. 1 A shows the timeline for the distribution of the variants detected in samples received from April 2021 to January 2022. The first cases were positive for non-VOCs, followed in the order presented herein by the Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529) variants. This variant evolution was similar to that reported worldwide: non-VOC infections followed by other variants (Alpha, Beta), followed by the Delta wave in the summer of 2021, followed by the Omicron wave in winter of 2021–2022.
Fig. 1.
Distribution of SARS-CoV-2 variants over time, by age and by vaccination dose. A) Density plot showing the percentage of samples with each variant over time: the first cases detected were non-VOCs; Delta dominated from November to the end of December; and Omicron began dominating in January. B) Distribution of variants by age group. C) Distribution of variants by vaccination status and dose.
Table 1 shows the analysis of patient clinical and demographic data for each variant. A significant association was detected by gender, with more male than female patients infected with Delta, but more female than male patients infected with Omicron. The mean age differed significantly by variant, with the oldest patients infected with non-VOCs (mean age, 43.4 years), whereas the youngest patients were infected with Omicron (mean age, 33.7 years). Most non-VOC, Alpha, and Delta variant infections were in patients aged 30–39 years; most Beta variant infections were in patients aged 40–49 years; and most Omicron variant infections were in patients aged 20–29 years (Fig. 1 B). No association was detected by symptom although most asymptomatic patients were infected with the Omicron variant. ICU admission was significantly associated with variant type, with the highest ICU admissions for patients infected with Delta and non-VOCs. A significant association between variant type and comorbidity was also found. Patients with no comorbidity were infected mainly with non-VOC, Alpha, and Beta variants, whereas patients with comorbidities were infected mainly with Omicron. There was no association between variant type and hypertension or diabetes. There was also no association between variant type and vaccination status. However, a significant association was detected between variant type and vaccine type, with patients who received BNT162b2 and mixed vaccines infected primarily with the Omicron variant, and patients who received ChAdOx1 infected primarily with Delta. A significant association was also found between variant type and number of vaccine doses, with the Delta variant detected primarily in patients after one dose, and Omicron being most common after the second and booster doses (Fig. 1 C).
Table 1.
Demographic and clinical characteristics of 900 PCR-confirmed SARS-CoV-2–infected patients with genomic samples sequenced, by detected variant.
| Characteristica |
Variant, No. (%) of 900 samples |
P-Value | ||||
|---|---|---|---|---|---|---|
| Non-VOC (n = 100,11.1 %) | Alpha (n = 37,4.1 %) | Beta (n = 50,5.6 %) | Delta (n = 191,21.2 %) | Omicron (n = 522,58 %) | ||
| Gender | ||||||
| Male (n = 407) | 51(5.7) | 15(1.7) | 21(2.4) | 112(12.5) | 208(23.2) | (0.0002)b |
| Female (n = 488) | 48(5.4) | 22(2.5) | 29(3.2) | 78(8.7) | 311(34.8) | |
| Age (Mean, SD), years | 43.4(15.5) | 40.7(18.4) | 35.9(14.7) | 40.4(12.9) | 33.7(16) | (0.0001)b |
| Age Group, years | ||||||
| 0–19 (n = 98) | 1(0.1) | 2(0.22) | 6(0.7) | 5(0.60) | 84(9.4) | (0.0001)b |
| 20–29 (n = 203) | 16(1.8) | 10(1.1) | 13(1.5) | 28(3.1) | 136(15.2) | |
| 30–39 (n = 251) | 29(3.2) | 11(1.2) | 10(1.1) | 67(3.1) | 134(15.0) | |
| 40–49 (n = 177) | 21(2.3) | 3(0.33) | 14(1.6) | 55(6.1) | 84(9.4) | |
| 50–59 (n = 102) | 19(2.1) | 7(0.8) | 5(0.60) | 20(2.2) | 51(5.7) | |
| 60–102 (n = 65) | 13(1.5) | 4(0.5) | 2(0.2) | 15(1.7) | 31(3.5) | |
| Symptomatic | ||||||
| Yes (n = 825) | 99(11) | 35(3.9) | 49(5.4) | 182(20.2) | 487(54.11) | (0.140) |
| No (n = 48) | 1(0.11) | 2(0.22) | 1(0.11) | 9(1) | 35(3.9) | |
| Intensive Care Unit Admittance | ||||||
| Yes (n = 21) | 7(0.90) | 0 | 1(0.13) | 10(1.30) | 3(0.40) | (0.0001)b |
| No (n = 516) | 42(5.4) | 29(3.8) | 38(4.9) | 128(16.5) | 516(66.7) | |
| Comorbidity | ||||||
| Yes (n = 359) | 25(2.8) | 4(0.44) | 7(0.80) | 47(5.2) | 276(30.7) | (0.0001)b |
| No (n = 540) | 74(8.2) | 33(3.70) | 43(4.8) | 144(16.0) | 246(27.4) | |
| Diabetes Mellitus Status | ||||||
| No (n = 825) | 90(10.0) | 36(4.0) | 49(5.5) | 177(16.7) | 473(52.6) | (0.25) |
| Yes (n = 74) | 9(1.0) | 1(0.1) | 1(0.1) | 14(1.6) | 49(5.5) | |
| Hypertension Status | ||||||
| No (n = 816) | 90(10.0) | 34(3.8) | 49(5.5) | 176(19.6) | 467(52.0) | (0.32) |
| Yes (n = 83) | 9(1.0) | 3(0.33) | 1(0.11) | 15(1.7) | 55(6.1) | |
| CT Value Range | ||||||
| High (n = 100) | 8(1.2) | 5(0.7) | 8(1.2) | 20(2.9) | 59(8.6) | (0.0001)b |
| Low (n = 154) | 13(1.9) | 2(0.30) | 8(1.2) | 42(6.1) | 89(13.0) | |
| Moderate (n = 430) | 14(2.1) | 17(2.5) | 14(2.1) | 63(9.2) | 322(47.1) | |
| Vaccination Status | ||||||
| Vaccinated (n = 584) | 57(7.3) | 23(2.9) | 35(4.50) | 146(18.8) | 323(41.6) | (0.22) |
| Unvaccinated (n = 193) | 21(2.7) | 10(1.3) | 7(0.90) | 36(4.6) | 119(15.3) | |
| Type of Vaccine | ||||||
| BNT162b2 (n = 281) | 23(4.1) | 3(0.5) | 14(2.5) | 60(10.6) | 181(31.9) | (0.0001)b |
| ChAdOx1 (n = 177) | 34(6.0) | 16(2.8) | 20(3.5) | 83(14.6) | 24(4.2) | |
| Mixture of vaccines and other types (n = 110) | 0 | 0 | 0 | 3(0.5) | 107(18.8) | |
| Vaccine Breakthrough | ||||||
| Post first dose (n = 241) | 53(10.0) | 19(3.6) | 30(5.7) | 111(21.0) | 28(5.3) | (0.0001)b |
| Post second dose (n = 267) | 2(0.40) | 2(0.4) | 5(1.0) | 25(4.7) | 233(44.1) | |
| Post Booster (n = 21) | 0 | 0 | 0 | 0 | 21(3.97) | |
Abbreviations: PCR, polymerase chain reaction; VOC, variant of concern.
Not all characteristics sum to 900 patients owing to missing data.
Denotes statistical significance at P < 0.05.
Vaccination status and dose by patient characteristic and clinical manifestation
Our second aim was to evaluate vaccination status and dose by patient demographic and clinical characteristics ( Table 2). There was a significant difference by age: unvaccinated patients were younger (mean age, 33.4 years), and vaccinated patients were older (mean age, 37.3 years). No significant difference was detected for gender or for cycle threshold (Ct) by vaccination status, with the mean Ct for both vaccinated and unvaccinated patients approximately 23. A significant association was found between vaccination status and age group, with fewer vaccinated patients in the 0–19 years age group but more vaccinated patients in all other age groups. An association was also detected between vaccination status and nationality, with a low proportion of unvaccinated non-Saudi patients (2.4 %). ICU admission was significantly associated with vaccination status, with unvaccinated patients having higher ICU admission than vaccinated patients (1.2 % vs. 0.7 %). Regarding comorbidities, among unvaccinated patients, a higher percentage had comorbid conditions (14 %), whereas among vaccinated patients, most patients did not have comorbidities (52 %). No significant association was found between vaccination status and patient symptom, diabetes, and hypertension.
Table 2.
Patient demographic and clinical characteristics by vaccination status and dose.
| Characteristica | Vaccination Status (n = 777, UNK=123) |
Breakthrough infection after vaccine dose (n = 529, UNK=55) |
|||||
|---|---|---|---|---|---|---|---|
| Unvaccinated (n = 193,24.8 %) | Vaccinated (n = 584,75.2 %) | (P-value) | First dose (n = 241,45.6 %) | Second dose (n = 267, 50.5 %) | Booster (n = 21,3.9 %) | (P-value) | |
| Age (mean, SD) years | 33.4(20.3) | 37.3(11.5) | (0.0011)b | 37.6(9.9) | 36.9(12.3) | 38.6(9.6) | (0.022)b |
| Ct (mean, SD) | 23.9(6.2) | 23.6(5.5) | (0.57) | 23.6(5.6) | 23.4(5.4) | 25(4.3) | (0.45) |
| Gender (n = 773) | |||||||
| Male (n = 354) | 84(10.9) | 270(34.9) | (0.56) | 133(25.6) | 99(19.0) | 12(2.3) | (0.0002)b |
| Female (n = 419) | 107(13.8) | 312(40.4) | 106(20.4) | 162(31.2) | 8(1.5) | ||
| Age Group (n = 774) years | |||||||
| (0–19) (n = 67) | 46(5.9) | 21(2.7) | (0.0001)b | 2(0.4) | 11(2.11) | 0 | (0.13) |
| (20–29) (n = 186) | 39(5.0) | 147(19.0) | 57(10.9) | 70(13.4) | 3(0.6) | ||
| (30–39) (n = 233) | 35(4.5) | 198(25.6) | 89(17.1) | 90(17.2) | 8(1.5) | ||
| (40–49) (n = 160) | 25(3.2) | 135(17.4) | 62(11.9) | 53(10.2) | 8(1.5) | ||
| (50–59) (n = 87) | 27(3.5) | 60(7.8) | 27(5.2) | 26(3.9) | 2(0.4) | ||
| (60–102) (n = 41) | 18(2.3) | 23(3.0) | 3(0.60) | 12(2.3) | 1(0.2) | ||
| Nationality (n = 715) | |||||||
| Saudi (n = 438) | 147(20.6) | 291(38.7) | (0.0001)b | 142(28.9) | 100(20.3) | 11(2.2) | (0.0001)b |
| Non-Saudi (n = 277) | 17(2.4) | 260(36.4) | 90(18.3) | 142(28.9) | 7(1.4) | ||
| Symptoms Observed (n = 777) | |||||||
| Asymptomatic (n = 45) | 10(1.3) | 35(4.5) | (0.67) | 10(1.9) | 16(3.1) | 3(0.60) | (0.11) |
| Symptomatic (n = 732) | 183(23.6) | 549(70.7) | 230(44.1) | 246(47.1) | 17(3.3) | ||
| ICU Admission (n = 672) | |||||||
| No (n = 659) | 167(24.9) | 492(73.2) | (0.0032)b | 158(36.0) | 258(58.8) | 20(4.5) | (0.54) |
| Yes (n = 13) | 8(1.2) | 5(0.7) | 2(0.46) | 1(0.2) | 0 | ||
| Comorbidity (n = 777) | |||||||
| No (n = 488) | 84(10.8) | 404(52.0) | (0.0001)b | 221(42.3) | 144(27.6) | 14(2.7) | (0.0001)b |
| Yes (n = 289) | 109(14.0) | 180(23.2) | 19(3.6) | 118(22.6) | 6(1.2) | ||
| Hypertension (n = 777) | |||||||
| No (n = 707) | 175(22.5) | 532(68.5) | (0.85) | 234(44.8) | 229(43.8) | 19(3.6) | (0.0001)b |
| Yes (n = 70) | 18(2.3) | 52(6.7) | 6(1.2) | 33(6.3) | 1(0.2) | ||
| Diabetes Mellitus (n = 777) | |||||||
| No (n = 712) | 175(22.5) | 537(69.1) | (0.57) | 235(45) | 232(44.4) | 19(3.6) | (0.0001)b |
| Yes (n = 65) | 18(2.3) | 47(6.1) | 5(1.0) | 30(5.6) | 1(0.2) | ||
| Ct Value Range (n = 607) | |||||||
| High (Ct > 30) (n = 86) | 23(3.8) | 63(10.4) | (0.73) | 28(7.0) | 23(5.7) | 3(0.8) | (0.0002)b |
| Low (20 <;Ct) (n = 144) | 33(5.4) | 111(18.3) | 46(11.4) | 51(12.7) | 4(1.0) | ||
| Moderate (20–30) (n = 377) | 98(16.1) | 279(46.0) | 64(15.9) | 172(42.8) | 11(2.7) | ||
Ct indicates cycle threshold; ICU, intensive care unit; UNK, unknown.
Not all characteristics sum to 900 patients owing to missing data.
P values< 0.05 considered statistically significant.
We further investigated patient demographic and clinical characteristics among vaccinated patients with BTIs by the number of vaccine doses they received ( Table 2 ). Age was significantly associated with vaccine dose, with the oldest patients (mean age, 38.6 years) receiving a booster dose, and the youngest (mean age, 36.9 years) receiving a second dose. By Ct group classification, a significant difference was detected, with the group having a Ct value> 30 composed mainly of patients receiving a single vaccine dose. A significant association was found for gender: BTIs were highest in male patients after the first and second doses, whereas BTIs were highest in female patients after the second dose. Nationality status was also significantly associated with BTIs. Many Saudi patients were infected after the first and second doses, whereas most non-Saudi patients were infected after the second dose. Although no significant association was found for ICU admission, no ICU cases were reported for patients with a booster dose. A significant association was found for comorbidity status. Most patients with comorbid conditions were infected after the second dose. For hypertension and diabetes, many patients were infected after the second dose, and only one patient with hypertension and one patient with diabetes were infected after a booster dose.
BTI and ICU admission risks by patient demographic and clinical characteristics
Our third aim was to identify risk factors associated with BTIs and with ICU admission ( Table 3 ). We conducted binary/univariate logistic regression analysis to estimate ORs. For risk of BTI, a significant association was found by age group, with the highest odds found in the age group of 30–39 years (OR = 12.4 CI 95 %: 6.6–23.2), followed by 40–49 years (OR = 11.2, CI 95 %: 6.1–23.1), and 20–29 years (OR = 8.2, CI 95 %: 4.4–15.4). Higher risk of BTI was found for non-Saudi patients (OR = 7.7, CI 95 %: 4.6–13.1) compared with Saudi patients and for patients with no comorbidity history (OR = 2.9, CI 95 %: 2.1–4.1), but not for gender, Ct classification, hypertension, diabetes, or symptoms. Global multivariate analysis was conducted to calculate adjusted ORs (age and comorbidity as confounder variables). The analysis showed that the global model was significant, with higher risk of BTI among non-Saudi patients (OR = 3.8, CI 95 %: 1.8–8.4) and reduced risk for patients with no hypertension (OR = 0.06, CI 95 %: 0.005–0.77).
Table 3.
Estimated odds of ICU admission and breakthrough COVID-19 infection by patient demographic and clinical characteristics.
| Characteristic | Breakthrough infection |
ICU Admission |
||||||
|---|---|---|---|---|---|---|---|---|
| OR (95 % CI) | (P-value) | AOR (95 % CI) | Global (P-value) | OR (95 % CI) | (P-value) | AOR (95 % CI) | Global (P-value) | |
| Gender | ||||||||
| Male | 1 | (0.56) | 1 | (0.0013)a | 1 | (0.1) | 1 | (0.055) |
| Female | 0.91(0.7–1.3) | 1.0(0.58–1.9) | 0.48(0.19–1.17) | 0.18(0.016–2.04) | ||||
| Age Group, years | ||||||||
| (0–19) | 1 | (0.0001)a | b | 1 | (0.0001)a | b | ||
| (20–29) | 8.2(4.4–15.4) | NA | ||||||
| (30–39) | 12.4(6.6–23.2) | 2.4(0.27–20.4) | ||||||
| (40–49) | 11.2(6.1–23.1) | 2.5(0.27–22.6) | ||||||
| (50–59) | 4.9(2.4–9.7) | 2.4(0.2–26.7) | ||||||
| (60–102) | 2.8(1.3–6.3) | 17.3(2.1–140.2) | ||||||
| Nationality | ||||||||
| Non-Saudi | 7.7(4.6–13.1) | (0.0001)a | 3.8(1.75–8.4) | NA | NA | NA | ||
| Saudi | 1 | 1 | NA | NA | ||||
| Ct Value Range | ||||||||
| High | 1 | (0.72) | 1 | 0.62(0.1–5.1) | (0.83) | 1.6(0.11–22.8) | ||
| Low | 1.2(0.66–2.3) | 0.90(0.36–2.3) | 1.2(0.31–4.7) | 1.4(0.1–16.9) | ||||
| Moderate | 1.0(0.6–1.8) | 1.3(0.56–2.9) | 1 | 1 | ||||
| Hypertension | ||||||||
| No | 1.1(0.6–1.8) | (0.85) | 0.06(0.005–0.77) | 0.17(0.07–0.42) | (0.0001)a | NA | ||
| Yes | 1 | 1 | 1 | |||||
| Diabetes Mellitus | ||||||||
| No | 1.2(0.65–2.1) | (0.58) | 5.7(0.53–65) | 0.12(0.05–0.31) | (0.0001)a | NA | ||
| Yes | 1 | 1 | 1 | |||||
| Comorbidity | (0.001)a | b | NA | b | ||||
| No | 2.9(2.1–4.1) | NA | ||||||
| Yes | 1 | NA | ||||||
| Symptoms | (0.67) | |||||||
| Asymptomatic | 1.2(0.56–2.4) | 2.3(0.66–7.8) | NA | NA | NA | |||
| Symptomatic | 1 | 1 | NA | |||||
CI indicates confidence interval; Ct, cycle threshold, OR, odds ratio; AOR, adjusted odds ratio; ICU, intensive care unit; and NA, data not available and not included in statistical analysis.
P values< 0.05 considered statistically significant.
AOR calculated from multivariate analysis using age and comorbidity as confounders.
For risk of ICU admission, a significant association was found by age group, with the highest odds found in the age group 60–102 years (OR = 17.3 CI 95 %: 2.1–140.2). Lower risk of ICU admission was found for patients without hypertension (OR = 0.17, CI 95 %: 0.07–0.42) and for patients without diabetes (OR = 0.12, CI 95 %: 0.05–0.31). There was no significant risk of ICU admission by gender or Ct classification. All admitted ICU patients were Saudi, all had comorbidities, and all presented with symptoms. Global multivariate analysis was conducted to calculate adjusted ORs, with age and comorbidity as confounder variables. The analysis showed that the global model was not significant after adjusting for the confounders.
Variants in the spike gene by patient demographic and clinical characteristics and by vaccination status and dose
Samples with good quality and coverage for variant calling were analyzed by patient characteristics. The main spike variants detected with high frequency in our cohort were H69del, Y144del, K417N, N440K, L452R, S477N, T478K, E484K, N501Y, D614G, and P681R ( Fig. 2 ). The variants with the highest frequencies were D614G (82.6 %), T478K (61.6 %), K417N (55.6 %), H69del (55.1 %), and N440K (50.9 %). The least frequent variants were E484K (7.2 %), Y144del (19.9 %), and P681R (23.1 %).
Fig. 2.
Heatmap showing spike variants by patient demographic and clinical characteristics. Heatmap columns represent detected variants, and rows represent the indicated characteristic. The frequency of each variant is shown in each square according to the color gradient given in the key, with purple being the lowest frequency and yellow being the highest.
A summary of each variant by patient demographic and clinical characteristics and by vaccination status and dose is given in Supplementary Table S1. Our analysis by age indicated that all but two spike variants were found in younger patients. Variants H69del, K417N, N440K, S477N and N501Y (Group A) were found primarily in younger patients (P < 0.05), whereas, L452R and P681R (Group B) were found primarily in older patients (P < 0.05). Group A variants had higher odds of being found in female patients, whereas group B variants showed lower odds. Group A variants were detected mostly in Saudi patients, and group B variants were detected mostly in non-Saudi patients. The highest viral load (i.e., lowest Ct) was found in patients with H69del, K417N, and D614G variants, and the lowest viral load (i.e., highest Ct) was found in patients with E484K, and N501Y variants.
Only three variants were significantly associated with vaccination status. Variant N440K was found primarily in unvaccinated patients (OR = 1.5, 95 % CI 1.1–2.1), whereas L452R (OR = 1.6, 95 % CI 1.1–2.4) and P681R (OR = 1.9, 95 % CI 1.1–3.1) were found primarily in vaccinated patients. Our analysis by vaccination dose indicated that after a booster shot, L452R, E484K, P681R were not detected in any patient. After the second dose, all variants except L452R, P681R, and E484K were associated with higher risk for BTI.
Phylogenetic analysis in outbreaks
The fourth aim of this study was to conduct phylogenetic and cluster formation analyses for outbreaks by BTI, ICU admission, and vaccination status. Fig. 3 shows the phylogenetic tree generated using the TIM2 + F + I substitution model. The analysis was conducted to reveal any patient interaction with multiple patient groups in nosocomial outbreaks. The tree differentiates into two major nodes: one node with Delta samples and the other with mostly Omicron samples. The first cluster (highlighted in light red in Fig. 3) indicated an outbreak in the hospital involving three ICU cases with four HCWs and seven unvaccinated individuals. The next cluster (highlighted in light brown) is divided into three main branches, with cases involving HCWs and regular cases detected in January. The third main cluster (highlighted in light green) starts with vaccinated HCWs and a regular case, and then branches into nine outbreaks. The next cluster (highlighted in Tiffany-blue) includes 7 HCWs, of which 3 were unvaccinated. The next cluster (highlighted in lavender) has two main branches. Of the five patients in the first branch, three were HCWs; of the 18 patients in branch B, 13 were HCWs. The second branch also shows HCW-HCW transmission as well as HCW-regular case transmission. In the next cluster (highlighted in light violet) of 23 patients, the first group has two regular cases that were vaccinated patients and a single HCW case, followed by the second group, which has mostly regular cases of vaccinated individuals. The second group has the most regular case to regular case transmission between vaccinated individuals. The next clusters (highlighted in light pink) branch from the same node to give rise to eight minor nodes. The first nodes have a couple of HCW-HCW transmissions giving rise to other clusters and cases related to vaccinated regular case to regular case transmission. The later nodes in the same cluster also involve 18 unvaccinated patients, which was the highest number across all other clusters. The last cluster in this analysis (highlighted in yellow) has more than 16 minor nodes within the same cluster. The cluster begins with a vaccinated HCW, who gives rise to multiple outbreaks of HCW-HCW transmissions and regular case to HCW transmissions; the majority of the cases in this cluster were vaccinated, with only 18 unvaccinated patients.
Fig. 3.
Phylogenetic analysis of transmission in a single center. The sequences shown in the figure are annotated to reflect patient data: sample ID, variant, month of infection, vaccinated (V) or unvaccinated (UV), healthcare worker (HCW) or not (N), and intensive care unit (ICU) or (R) regular case. Overall, there were 8 main clusters using the TIM2 + F + I model.
To further investigate clusters, we conducted the analysis again using the IQ-TREE-2 tool but with the TIM2 + F + I model. The visualized model is shown in Fig. 4. The total cluster with 21 patients has 2 major nodes: one node has the most cases (red); the other node has only three cases (yellow). The first minor node (yellow) has a total of three patients carrying the Delta variant, of whom two were vaccinated and none was an HCW. The second minor node (red and purple) has 18 patients comprising three ICU cases, five unvaccinated patients, and four HCWs. ICU case sample number 161 was closely related to case sample number 40, which was an unvaccinated patient. The second ICU case, sample number 50, was very close to HCW case sample number 69 and to unvaccinated case sample number 550. The third ICU case, sample number 28, was related closely to a vaccinated patient (sample number 48), who was not an HCW. Overall, Our analysis highlights the interactions among multiple cases associated with nosocomial outbreaks for ICU cases, unvaccinated individuals, and HCWs.
Fig. 4.
Phylogenetic analysis for outbreaks including ICU patients. The sequences shown in the figure were annotated to reflect patient data: sample ID, variant, month of infection, vaccinated (V) or unvaccinated (UV), healthcare worker (HCW) or not (N), and intensive care unit (ICU) or (R) regular case. Overall, there were 21 patients, and the estimated tree was constructed using the TIM2 + F + I model. The estimated branch length is shown in each node.
Discussion
The outcome of this study showed the SARS-CoV-2 community transmission and genomic evolution during the Delta and Omicron waves at a tertiary hospital in Saudi Arabia. We were able to track variant epidemiology by clinical variables, evaluate vaccination status and dose by patient demographic and clinical characteristics, identify risk factors for BTIs and ICU admission, and conduct phylogenetic analysis and cluster formation for outbreaks by patient group.
Variant distributions by patient demographic and clinical characteristics were significantly different in our cohort. By gender, the Delta wave had more male than female cases, whereas more female than male cases were detected in the Omicron wave. The oldest patients were infected with non-VOCs, whereas the youngest cases were infected with Omicron strains. Another study comparing Delta and Omicron waves conducted in France also observed Omicron infections predominantly in younger, female, and vaccinated patients [23]. That study also noted that compared with Delta infection, Omicron infection was independently associated with lower risk for ICU admission, mechanical ventilation, and in-hospital admission. Similar observations have been reported in many other countries, including the United States, Denmark, Qatar, and England, suggesting that the Omicron wave is associated with less disease severity than the Delta wave [22], [23], [24], [25], [26], [27]. However, a comparison of the vaccination coverage indicated that the Omicron wave infected populations with higher vaccination coverage than the Delta wave. Vaccination remains one of the most critical factors in COVID-19 disease severity. Interestingly, on the basis of the findings in our cohort, the Delta and Omicron waves in the Saudi population showed significantly different patient characteristics and disease severity. However, the comparison of severity may be biased because during the Omicron wave, the population had a higher percentage of vaccine coverage, reaching 70 % in 2022.
In our cohort, younger patients were mainly unvaccinated, whereas elderly patients were mostly vaccinated; this difference was statistically significant. The highest risk for BTIs by age in our cohort was found in the group 30–39 years of age, followed by 40–49 years and 20–29 years. This risk profile may be explained in part by the Saudi government's vaccination priority list, which opened first for elderly and high-risk groups. Similar results for BTIs were also observed in a population in the United States [24]. ICU admission was significantly associated with vaccination status, with the highest ICU admissions reported for unvaccinated patients, and no ICU cases reported among individuals who received a booster. Other studies have also found that patients with boosters were more protected than unvaccinated patients and patients who received only two vaccine doses; this association was found for both Omicron and Delta variants [28].
The BTIs among hospitalized patients tend to be in elderly individuals with comorbidities [29]. Additionally, the higher risk for Omicron infection suggests less protection against the Omicron than the Delta variant. Although the many variants of Omicron make the virus efficient and able to cause BTIs, most Omicron cases were associated with fewer deaths and lower hospital admission rates [30].
The variants most frequently detected in our cohort were D614G (82.6 %), followed by T478K (61.6 %), and K417N (55.6 %). In our previous work, we also reported a high percentage of D614G variants in a Saudi population with a higher risk of hospital admission [18] . In the present study, we observed variant sets that were associated with specific patient characteristics. For example, the H69del, K417N, N440K, S477N, and N501Y variants were primarily found in younger, female, and Saudi (vs. non-Saudi) patients. By contrast, L452R and P681R variants were mostly found in elderly, male, and non-Saudi patients. The highest viral load was found in patients with H69del, K417N, and D614G variants. Most BTI cases in our cohort had either the L452R or P681R variant. No samples with variants H69del, L452R, E484K, P681R, and D614G were from patients who received a booster dose. This finding indicates that receipt of a booster vaccine in our cohort was effective against variants that harbor those mutations. The results from other studies have indicated that spike variants N501Y and H69del increase disease severity and virus transmissibility [31], and that the L452R variant increases virus infectivity and is associated with escape from neutralizing antibodies [32]. Several variants found in Omicron (i.e., H69del, Y144del, K417N, T478K, N501Y, D614G, and P681R) have been linked to immune escape elicited by previous infection or vaccine [33].
Our phylogenetic analysis highlighted the interactions among multiple cases associated with outbreaks for BTIs, ICU admission, and HCWs. We identified eight distinctive clusters. The first cluster included all the Delta cases and had all the ICU admission cases. In this outbreak, most ICU cases were closely related to unvaccinated individuals. Most HCW outbreaks were found in clusters, primarily evolving through HCW-HCW transmission that then moved to other individuals. Unvaccinated patients also significantly transmitted the virus by regular case-to-unvaccinated transmission, HCW-to-vaccinated transmission, and unvaccinated-to-unvaccinated transmission. In a large meta-analysis study that included 230,398 HCWs, the estimated prevalence of SARS-CoV-2 infection was 11 % [34]. The most frequently affected personnel were nurses working in hospitalization/non-emergency wards during the screening phase. Another study that evaluated suspected risk factors for outbreaks among HCW staff recognized a higher risk of failing to use personnel protective equipment while caring for patients with COVID-19 infection [35]. HCW transmission to hospitalized patients caused approximately a fifth of identified cases among hospitalized patients with COVID-19 in the “first wave” in England [36]. In many countries, current surveillance underestimates the strength of outbreaks [37]. Using modeling, one study found that exposure to an infectious patient with hospital-acquired SARS-CoV-2 or to an infectious HCW was associated with an additional 0.8 infections per 1000 susceptible HCWs per day [38]. By contrast, exposure to an infectious patient with community-acquired SARS-CoV-2 was associated with less than half the reported risk. These results indicate the importance of genomic surveillance in health care setting to limit community-acquired SARS-CoV-2 infections.
Study limitations
A key limitation in our study was that we did not conduct epidemiological/tracing interviews to confirm the transmission modes and whether the infections among HCWs were acquired in the hospital or community. Samples sizes for the analysis of patient distribution by variant was not equal, which could bias our results due to having more vaccinated Omicron cases than others. Future studies assessing additional data on HCW roles and their interaction with patients would enhance hospital infection control procedures. In our future work to further investigate virus evolution, we plan to conduct genomic surveillance at the hospital alongside epidemiologic interviews to create a phylogenetic database that links patients involved in outbreaks.
Conclusions
To the best of our knowledge, this is the largest SARS-CoV-2 genomic surveillance study conducted in the region. Our findings on the distribution of Delta and Omicron variants across patient demographic and clinical characteristics and by vaccination status have several important public health implications. Our cohort included many BTIs, enabling investigation of the genomic evolution of the virus. Our data highlight the importance of vaccination, especially to reduce the severity of COIVD-19 disease. These results emphasize the importance of genomic surveillance for infectious diseases, especially in healthcare settings.
Ethics approval
The study protocol was approved by The Research Advisory Council (Clinical Research Committee) (RAC #220 0009).
Funding
This work was supported by King Faisal Specialist Hospital and Research Centre Grant no. 220 0009, 2020].
Competing interests
The authors declare that the research was conducted in the absence of any potential conflict of interest.
Footnotes
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jiph.2022.12.007.
Appendix A. Supplementary material
Supplementary material
Data Availability
The data and codes used in this study are available upon request. In addition, the SARS-CoV-2 sequences are deposited in GISAID website.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material
Data Availability Statement
The data and codes used in this study are available upon request. In addition, the SARS-CoV-2 sequences are deposited in GISAID website.