ABSTRACT
Background. Global genomic surveillance has allowed identification of SARS-CoV-2 circulating variants responsible for the COVID-19 pandemic. Statewide variant characterization can guide local public health mitigations and provide educational opportunities. We characterized statewide evolution of SARS-CoV-2 variants in Rhode Island (RI). Methods. De identified RI SARS-CoV-2 sequences since 2/2020, generated at authors, CDC and commercial laboratories, were extracted from https://www.gisaid. org. Genomic and phylogenetic analyses were conducted with available tools and custom python scripts and, after quality control, sequences were classified as variants of Concern (VOC), variants being monitored (VBM), or non-VOC/ non-VBM, per CDC definitions. Specific mutations that are characteristic of the most recent VOCs (Delta or Omicron) were explored outside of their designated lineages. Results. Of the 1.1 million RI population, 14,933 SARS-CoV-2 sequences were available between 2/2020 and 3/2022. These included 1,542 (11%) sequences from 37 non-VOC/non-VBM lineages until 2/2021, most commonly B.1.2 (21%), B.1.375 (13%), and B.1.517 (6%);2,910 (19%) sequences from 7VBM lineages between 3-6/2021, most commonly Alpha (48%), Iota (34%), and Gamma (10%);and 10,481 (70%) sequences from 2 VOC lineages, including 7,574 (72%) Delta mostly between 6/2021 and 12/2021, and 2,907 (28%) Omicron mostly between 1/2022 and 3/2022. Phylogeny showed expected clustering of local variants within regional and global sequences, and continued viral evolution over time. Further VOC evolution was observed, including 87 Delta sub-lineages, most commonly AY.103 (17%), AY.3 (15%), and AY.44 (12%);and 4 Omicron sub-lineages BA.1 (61%), BA.1.1 (32%), BA.2 (7%), and BA.3 (< 1%). Omicron-associated mutations S:del69/70, S:H655Y, or N: P13L were observed in 219 Delta sequences, and Delta-associated mutations ORF1b: G662S, N:D377Y, or M:I82T were observed in 16 Omicron sequences. Conclusion. Statewide SARS-CoV-2 genomic surveillance allows for continued characterization of locally circulating variants and monitoring of viral evolution. Such data guide public health policies, inform the local health force, and mitigate the impact of SARS-CoV-2 on public health.
ABSTRACT
Background. SARS-CoV-2 seroprevalence studies can inform pandemic spread. By February 2021, estimates demonstrated 11%-62% seroprevalence in diverse Kenyan populations, with geographic variability and temporal increase, and well in excess of 0.2% laboratory-confirmed cases. The impact of HIV on seropositivity, particularly in youth living with HIV (YLWH) is unclear. Methods. During February to September 2021, before SARS-CoV-2 vaccination, we cross-sectionally enrolled perinatally-infected YLWH in western Kenya in four sites (Eldoret, tertiary referral center;urban Kitale, peri-urban Turbo, rural Webuye), and determined seropositivity using the Bio-Rad Platelia assay. Additional evaluations included HIV viral load (VL), CD4 and a COVID-19-focused survey. Multiple logistic regression was used to measure associations of seropositivity with age, gender, enrollment month, site, HIV treatment failure (VL > 1,000 copies/ml), and CD4 (>= 500 vs < 500 cells/muL). Results. Of 241 YLWH, 29% were seropositive, 68% seronegative and 4% equivocal. Temporal trends (linear relationship per subsequent enrollment month;Odds Ratio (OR) 1.29 [95% Confidence Interval (CI), 1.06-1.58], p=0.013) and geographic variability (Eldoret-25%, Kitale-20%, Turbo-25%, Webuye-56%;p=0.027) were observed. Presumptive or laboratory-confirmed COVID-19 diagnosis, hospitalization, or death were absent. Self-reported illness was similar among seropositives and seronegatives, and highest in Webuye. Seropositivity was significantly associated with being male (OR, 1.06 [95% CI, 0.57-1.98], p=0.848), and age 15-17 years vs < 15 (OR, 2.57 [95% CI, 1.16-5.93], p=0.023), not with VL or CD4. Among seropositives, aboverange titers were seen in 57%. Conclusion. Of 241 Kenyan YLWH, 29% were SARS-CoV-2 seropositive by August 2021, with geographical, temporal, and age differences, and most seropositives mounting a robust response. Increased prevalence in rural Webuye may reflect less widespread mask-wearing, or its location on a busy transit route. Speculations on why seropositivity is low compared to earlier estimations, like HIV status, failed seroconversion, waning immunity, perception of risk promoting adherence to mitigations, or exposure to research-related guidance, should be investigated.
ABSTRACT
Background. Two years into the pandemic, clinicians do not have access to a standardized measurement of SARS-CoV-2 viral load (VL) that allows for VL comparison across clinical specimens and different assays. Reliable VL measurement in diverse respiratory specimens, over time, and in response to treatments such as remdesivir (RDV), could better inform treatment and prevention. Methods. To investigate the use of a standardized VL assay in respiratory specimens, we enrolled patients hospitalized with COVID-19 in Providence, RI, with/ without RDV exposure;collected serial samples from 4 compartments (nasopharyngeal-NP, nasal-NA, oropharyngeal-OP, saliva-SA) in 3 visits during the 1st week of hospitalization;and characterized SARS-CoV-2 VL using a ChromaCode HDPCRTM quantitative research use only assay, calibrated to the first World Health Organization (WHO) International Standard (IS). Linear mixed effects models and associated regression coefficients were used to analyze intercompartmental VL differences at enrollment, over time, and with/without RDV. Results. Of 35 participants (60% male;70% White, 14% Hispanic/Latino, 49% RDV exposure), all had visit 1 samples (median hospital day 1, IQR 0-2;pre-RDV for those exposed);80% visit 2 samples (median hospital day 2, IQR 1-8);and 37% visit 3 samples (median hospital day 4, IQR 3-7). Overall, 38 NP, 67 NA, 57 OP, and 67 SA samples were collected. Mean log VLs (Log10IU/mL) differed by compartment at visit 1 (NP 6.3, NA 4.9, OP 4.1, SA 5.6, p=0.0001) and significantly decreased over time in all compartments (p< 0.04 for all comparisons). Log VL change over time was not significantly different between compartments or between people treated/not treated with RDV. Conclusion. We successfully measured respiratory intercompartmental SARS-CoV-2 VL differences among hospitalized patients using a standardized assay calibrated to the WHO IS. Dissemination of standardized VL measurement methods will allow accurate VL comparisons across assay types quantified in IU/mL and improve assessment of the impact of COVID-19 treatments. Inter-compartmental VL differences at baseline may indicate sampling variability or different viral burden. RDV did not appear to accelerate viral decay.
ABSTRACT
COVID-19 is a worldwide public health emergency caused by SARS-CoV-2. Genomic surveillance of SARS-CoV-2 emerging variants is important for pandemic monitoring and informing public health responses. Through an interstate academic-public health partnership, we established Rhode Island's capacity to sequence SARS-CoV-2 genomes and created a systematic surveillance program to monitor the prevalence of SARS-CoV-2 variants in the state. We describe circulating SARS-CoV-2 lineages in Rhode Island;provide a timeline for the emerging and expanding contribution of variants of concern (VOC) and variants of interest (VOI), from their first introduction to their eventual predominance over other lineages;and outline the frequent identification of known adaptively beneficial spike protein mutations that appear to have independently arisen in non-VOC/non-VOI lineages. Overall, the described Rhode Island- centric genomic surveillance initiative provides a valuable perspective on SARS-CoV-2 in the state and contributes data of interest for future epidemiological studies and state-to-state comparisons.