Cross-sectional Ct distributions from qPCR tests can provide an early warning signal for the spread of COVID-19 in communities (preprint)

Background: SARS-CoV-2 PCR testing data has been widely used for COVID-19 surveillance. Existing COVID-19 forecasting models mainly rely on case counts, even though the binary PCR results provide a limited picture of the pandemic trajectory. Most forecasting models have failed to accurately predict the COVID-19 waves before they occur. Recently a model utilizing cross-sectional population cycle threshold (Ct) values obtained from PCR tests (Ct-based model) was developed to overcome the limitations of using only binary PCR results. In this study, we aimed to improve on COVID-19 forecasting models using features derived from the Ct-based model, to detect epidemic waves earlier than case-based trajectories. Methods: PCR data was collected weekly at Northeastern University (NU) between August 2020 and January 2022. The NU campus epidemic trajectories were generated from the campus incidence rates. In addition, epidemic trajectories were generated for Suffolk County, where NU is located, based on publicly available case-counts. A novel forecasting approach was developed by enhancing a recent deep learning model with Ct-based features, along with the default features of the model. For this, cross-sectional Ct values from PCR data were used to generate Ct-based epidemic trajectories, including effective reproductive rate (Rt) and incidence. The improvement in forecasting performance was compared using absolute errors and residual squared errors with respect to actual observed cases at the 7-day and 14-day forecasting horizons. The model was also tested prospectively over the period January 2022 to April 2022. Results: Rt estimates from the Ct-based model preceded NU campus and Suffolk County cases by 12 and 14 days respectively, with a three-way synched Spearman correlation of 0.57. Enhancing the forecasting models with Ct-based information significantly decreased absolute error and residual squared error compared to the original model without Ct features (p-value <0.001 for both 7 and 14-days forecasting horizons). Conclusion: Ct-based epidemic trajectories can herald an earlier signal for impending epidemic waves in the community and forecast transmission peaks. Moreover, COVID-19 forecasting models can be enhanced using these Ct features to improve their forecasting accuracy. Policy implications: We make the case that public health agencies should publish Ct values along with the binary positive/negative PCR results. Early and accurate forecasting

Epidemiological Interactions of Influenza and SARS-CoV-2 within a University Population During Omicron B.1.1.529 Outbreak (preprint)

The COVID-19 Pandemic has prompted innovation and research to further understand not only SARS-CoV-2, but other respiratory viruses as well. Since the start of the pandemic there has been a lack in influenza collection and surveillance. In October 2021 the Life Sciences Testing Center at Northeastern University implemented the TaqPath COVID-19, Flu A, Flu B combo kit to test for multiple respiratory diseases among the Universitys population. During this time the SARS-CoV-2 variant of concern, Omicron B.1.1.529, became the dominant strain in the greater Boston area. During this time an inverse correlation in the detection of positive SARS-CoV-2 and Influenza A was observed. More data is needed to determine if this observed inverse correlation on positivity rate is linked to public health measures or biological mechanism within the immune system.

SARS-CoV-2 Variant Surveillance Using Tandem Targeted RT-PCR-based Genotyping Assays and Whole Genome Sequencing (preprint)

Genomic surveillance is critical for tracking SARS-CoV-2 Variants of Concern (VOC) and for rapid detection of emerging variants. Whole genome sequencing (WGS) is the predominant method for genomic surveillance; but it is a laborious process for large-scale testing. The aim of this study was to assess the performance of a PCR-based mutation panel for the discrimination of 5 known VOC; Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2) and Omicron (B.1.1.529). Genotyping analysis was performed on 128 SARS-CoV-2 positive samples collected at the Life Science Testing Center at Northeastern University from April-December 2021. RNA extraction was performed using MagMax Viral/Pathogen II Nucleic Acid Isolation Kit. SARS-CoV-2 detection was confirmed using the TaqPath COVID-19 Combo Kit. Variant determination was conducted using a panel of TaqMan SARS-CoV-2 single nucleotide polymorphism (SNP) assays. On November 25, 2021, the emerging VOC (omicron) was reported by South Africa and the panel was quickly modified to detect omicron by substituting P681H and K417N assays. Based on the SNP panel analysis, variant identification in 128 samples were as follows: Alpha (N=34), Beta (N=1), Gamma (N=7), Delta (N=41) and Omicron (N=21). The genotyping panel accurately assigned lineages to all samples, confirmed by Ion Torrent GeneStudio S5 WGS. VOC discrimination using RT-PCR genotyping is a rapid, versatile method for detecting known and emerging SARS-CoV-2 variants. The versatility of SNP panels allows monitoring of emerging strains by simple layout adaptations. RT-PCR genotyping assays can expedite variant identification, enable high-throughput variant surveillance, and support WGS prioritization for detection of new variants.

Early introduction and rise of the Omicron SARS-CoV-2 variant in highly vaccinated university populations (preprint)

The Omicron variant of SARS-CoV-2 is transmissible in vaccinated and unvaccinated populations. Here, we describe the rapid dominance of Omicron following its introduction to three Massachusetts universities with asymptomatic surveillance programs. We find that Omicron was established and reached fixation earlier on these campuses than in Massachusetts or New England as a whole, rapidly outcompeting Delta despite its association with lower viral loads. These findings highlight the transmissibility of Omicron and its propensity to fixate in small populations, as well as the ability of robust asymptomatic surveillance programs to offer early insights into the dynamics of pathogen arrival and spread.

Performance of the TaqMan COVID-19 Pooling Kit for detection of SARS-CoV-2 in Asymptomatic and Symptomatic populations at an Institution of Higher Education (preprint)

Clinical evidence for asymptomatic cases of coronavirus disease (COVID-19) has reinforced the significance of effective surveillance testing programs. Quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) assays are considered the gold standard for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA. However, the labor and resource requirements can be prohibitive with respect to large testing volumes associated with the pandemic. Pooled testing algorithms may serve to increase testing capacity with more efficient resource utilization. Due to the lack of carefully curated cohorts, there is limited evidence for the applicability of RT-PCR pooling in asymptomatic COVID-19 cases. In this study, we compared the analytical sensitivity of the TaqMan SARS-CoV-2 Pooling Assay to detect one positive sample in a pool of five anterior nare swabs in symptomatic and asymptomatic cohorts at an institute of higher education. Positive pools were deconvoluted and each individual sample was retested using the TaqPath COVID-19 Combo Kit. Both assays target the open reading frame (ORF) 1ab, nucleocapsid (N), and spike (S) gene of the strain that originated in Wuhan, Hubei, China. Qualitative results demonstrated absolute agreement between pooled and deconvoluted samples in both cohorts. Independent t-test performed on Ct shifts confirmed an insignificant difference between cohorts with p-values of 0.306 (Orf1ab), 0.147 (N), and 0.052 (S). All negative pools were correctly reported as negative. Thus, pooled PCR testing up to five samples is a valid method for surveillance testing of students and staff in a university setting, especially when the prevalence is expected to be low.

Two Stage Hierarchical Group Testing Strategy to Increase SARS-CoV-2 Testing Capacity at an Institution of Higher Education: A Retrospective Analysis (preprint)

Population testing for severe acute respiratory syndrome 2 (SAR-CoV-2) is necessary owing to the possibility of viral transmission from asymptomatic cases, yet scarcity of reagents and equipment has added to the cost-prohibitive implementation of screening campaigns at institutions of higher education. The high analytical sensitivities of leading nucleic acid amplification diagnostic methods allow for group testing to increase testing capacity. A feasibility study was performed using an optimized testing configuration model for pooling three, five, and ten samples. Following the standard RNA extraction and purification workflow for quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) method using Thermo Fisher TaqPath™ COVID-19 multiplex primers and probes for the ORF1ab, N, and S genes, matrix and dilution effects were assessed using pooled negative samples as the diluent. Probit analysis produced a limit of detection of 16075 (ORF1ab), 1308 (N), and 1180182 (S) genomic copy equivalents per milliliter. Trials comparing neat to 1:5 dilution for 34 weak-to-strongly positive samples demonstrated average threshold cycle (CT) shifts of 2.31±1.16 (ORF1ab), 2.23±1.12 (N), and 2.79±1.40 (S). Notwithstanding observed S gene dropouts, the false negative rate was unaffected. As the ratio of asymptomatic positive to symptomatic positive SARS-CoV-2 infected individuals was approximately 4:1 and the average prevalence was 0.16% since we started testing in August 2020, pooled testing was identified as a viable, cost-effective option for monitoring the Northeastern University community.