Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data (preprint)

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic "novel" lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances, and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1% frequency, results were more reliable above a 5% threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of noise or bias in wastewater sequencing data and to appreciate the commonalities and differences across methods.

Altered structural connectivity in olfactory disfunction after mild COVID-19 using probabilistic tractography (preprint)

We aimed to investigate changes in olfactory bulb volume and brain network in the white matter (WM) in patients with persistent olfactory disfunction (OD) following COVID-19. A cross-sectional study evaluated 38 participants with OD after mild COVID-19 and 24 controls, including Sniffin' Sticks identification test (SS-16), MoCA, and brain magnetic resonance imaging. Network-Based Statistics (NBS) and graph theoretical analysis were used to explore the WM. The COVID-19 group had reduced olfactory bulb volume compared to controls. In NBS, COVID-19 patients showed increased structural connectivity in a subnetwork comprising parietal brain regions. Regarding global network topological properties, patients exhibited lower global and local efficiency and higher assortativity than controls. Concerning local network topological properties, patients had reduced local efficiency (left lateral orbital gyrus and pallidum), increased clustering (left lateral orbital gyrus), increased nodal strength (right anterior orbital gyrus), and reduced nodal strength (left amygdala). SS-16 test score was negatively correlated with clustering of whole-brain WM in the COVID-19 group. Thus, patients with OD after COVID-19 had relevant WM network dysfunction with increased connectivity in the parietal sensory cortex. Reduced integration and increased segregation are observed within olfactory-related brain areas might be due to compensatory plasticity mechanisms devoted to recovering olfactory function.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism (preprint)

Wastewater-based surveillance (WBS) is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19 impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with local workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.3 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5% (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4,524 unrelated absences COVID-19 cases were recorded. Employee absences significantly increased as wastewater signal increased through pandemic waves. Strong correlations between COVID-19-confirmed absences and wastewater SARS-CoV-2 signals (N1 gene: r=0.824, p<0.0001 and N2 gene: r=0.826, p<0.0001) were observed. Linear regression with adjusted R2-value demonstrated a robust association (adjusted R2=0.783), when adjusted by 7 days, indicating wastewater provides a one-week leading signal. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P<0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

Neurosurgical Services in the Northern Zone of Sarawak in Malaysia: The Way Forward Amid the COVID-19 Pandemic.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) pandemic has set a huge challenge to the delivery of neurosurgical services, including the transfer of patients. We aimed to share our strategy in handling neurosurgical emergencies at a remote center in Borneo island. Our objectives included discussing the logistic and geographic challenges faced during the COVID-19 pandemic. METHODS: Miri General Hospital is a remote center in Sarawak, Malaysia, serving a population with difficult access to neurosurgical services. Two neurosurgeons were stationed here on a rotational basis every fortnight during the pandemic to handle neurosurgical cases. Patients were triaged depending on their urgent needs for surgery or transfer to a neurosurgical center and managed accordingly. All patients were screened for potential risk of contracting COVID-19 prior to the surgery. Based on this, the level of personal protective equipment required for the health care workers involved was determined. RESULTS: During the initial 6 weeks of the Movement Control Order in Malaysia, there were 50 urgent neurosurgical consultations. Twenty patients (40%) required emergency surgery or intervention. There were 9 vascular (45%), 5 trauma (25%), 4 tumor (20%), and 2 hydrocephalus cases (10%). Eighteen patients were operated at Miri General Hospital, among whom 17 (94.4%) survived. Ninety percent of anticipated transfers were avoided. None of the medical staff acquired COVID-19. CONCLUSIONS: This framework allowed timely intervention for neurosurgical emergencies (within a safe limit), minimized transfer, and enabled uninterrupted neurosurgical services at a remote center with difficult access to neurosurgical care during a pandemic.

Agent based simulators for epidemic modelling: Simulating larger models using smaller ones (preprint)

Agent-based simulators (ABS) are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an epidemic in a city (or a region). They provide the flexibility to accurately model a heterogeneous population with time and location varying, person-specific interactions as well as detailed governmental mobility restrictions. Typically, for accuracy, each person is modelled separately. This however may make computational time prohibitive when the city population and the simulated time is large. In this paper, we dig deeper into the underlying probabilistic structure of a generic, locally detailed ABS for epidemiology to arrive at modifications that allow smaller models (models with less number of agents) to give accurate statistics for larger ones, thus substantially speeding up the simulation. We observe that simply considering a smaller aggregate model and scaling up the output leads to inaccuracies. We exploit the observation that in the initial disease spread phase, the starting infections create a family tree of infected individuals more-or-less independent of the other trees and are modelled well as a multi-type super-critical branching process. Further, although this branching process grows exponentially, the relative proportions amongst the population types stabilise quickly. Once enough people have been infected, the future evolution of the epidemic is closely approximated by its mean field limit with a random starting state. We build upon these insights to develop a shifted, scaled and restart-based algorithm that accurately evaluates the ABS's performance using a much smaller model while carefully reducing the bias that may otherwise arise. We apply our algorithm for Covid-19 epidemic in a city and theoretically support the proposed algorithm through an asymptotic analysis where the population size increases to infinity.

SARS-CoV-2 nucleocapsid protein inhibits the stress response through RNA-binding domain N2b (preprint)

The nucleocapsid protein N of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enwraps and condenses the viral genome for packaging but is also an antagonist of the innate antiviral defense. It suppresses the integrated stress response (ISR), purportedly by interacting with stress granule (SG) assembly factors G3BP1 and 2, and inhibits type I interferon responses. To elucidate its mode of action, we systematically deleted and over-expressed distinct regions and domains. We show that N via domain N2b blocks PKR-mediated ISR activation, as measured by suppression of ISR-induced translational arrest and SG formation. N2b mutations that prevent dsRNA binding abrogate these activities also when introduced in the intact N protein. Substitutions reported to block post-translation modifications of N or its interaction with G3BP1/2 did not have a detectable additive effect. In an encephalomyocarditis virus-based infection model, N2b - but not a derivative defective in RNA binding - prevented PKR activation, inhibited {beta}-interferon expression and promoted virus replication. Apparently, SARS-CoV-2 N inhibits innate immunity by sequestering dsRNA to prevent activation of PKR and RIG-I-like receptors. Observations made for the N protein of human coronavirus 229E suggests that this may be a general trait conserved among members of other orthocoronavirus (sub)genera. SIGNIFICANCE STATEMENTSARS-CoV-2 nucleocapsid protein N is an antagonist of innate immunity but how it averts virus detection by intracellular sensors remains subject to debate. We provide evidence that SARS-CoV-2 N, by sequestering dsRNA through domain N2b, prevents PKR-mediated activation of the integrated stress response as well as detection by RIG-I-like receptors and ensuing type I interferon expression. This function, conserved in human coronavirus 229E, is not affected by mutations that prevent posttranslational modifications, previously implicated in immune evasion, or that target its binding to stress granule scaffold proteins. Our findings further our understanding of how SARS-CoV-2 evades innate immunity, how this may drive viral evolution and why increased N expression may have been a selective advantage to SARS-CoV-2 variants of concern.

How effective was Newfoundland & Labrador's travel ban to prevent the spread of COVID-19? An agent-based analysis (preprint)

Background: To prevent the spread of COVID-19 in Newfoundland & Labrador (NL), NL implemented a wide travel ban in May 2020. We estimate the effectiveness this travel ban using a customized agent-based simulation (ABS). Methods: We built an individual-level ABS to simulate the movements and behaviors of every member of the NL population, including arriving and departing travellers. The model considers individual properties (spatial location, age, comorbidities) and movements between environments, as well as age-based disease transmission with pre-symptomatic, symptomatic, and asymptomatic transmission rates. We examine low, medium, and high travel volume, traveller infection rates, and traveller quarantine compliance rates to determine the effect of travellers on COVID spread, and the ability of contact tracing to contain outbreaks. Results: Infected travellers increased COVID cases by 4-74x times and peak hospitalizations by 4-96x, without contact tracing. Although contact tracing was highly effective at reducing spread, it was insufficient to stop outbreaks caused by travellers in even the best-case scenario, and the likelihood of exceeding contact tracing capacity was a concern in most scenarios. Quarantine compliance had only a small impact on COVID spread; travel volume and infection rate drove spread. Interpretation: NL's travel ban was likely a critically important intervention to prevent COVID spread. Even a small number of infected travellers can play a significant role in introducing new chains of transmission, resulting in exponential community spread and significant increases in hospitalizations, while outpacing contact tracing capabilities. With the presence of more transmissible variants, e.g., the UK variant, prevention of imported cases is even more critical.

Computation Tomography (CT)-Based Evaluation of Temporal Changes in Lung Abnormalities During the Recovery Stage in COVID-19 In-Hospital Patients (preprint)

Objectives: To assess the late phase CT changes of COVID-19 patients, and figure out factors predicting lung abnormality in late phase.Methods: We conducted a retrospective study on 42 patients (14 males, 28 females; age 65±10 years) with COVID-19 admitted between February 7, 2020 and March 27, 2020. Only patients with at least 3 CT scans taken at least 3 weeks after initial symptom onset were included in the study. CT images were analyzed by 2 independent radiologists using different scoring: (1) area-based scoring (ABS); and (2) intensity-weighted scoring (IWS). Temporal changes in the average lung lesion were evaluated by averaged area under the curve (AUC) of the CT score-time curve. Correlations between averaged AUCs and clinical characteristics were determined. Results: Temporal changes in lung abnormalities during recovery (weeks 3 through 8) of CT findings using the ABS system were variable (P=0.934). By contrast, the IWS system detected more subtle changes in lung abnormalities during the late phase of recovery in COVID-19 patients, with consistent week-to-week relative reductions in IWS (P=0.025). In assessing the correlation between averaged AUCs and clinical characteristics, strong relationships were observed with D-dimer and C-reactive protein (CRP) levels on admission, with hazard ratios (HR)(95%CI) of 5.32 (1.25-22.6)(P=0.026) and 1.05 (1.10-1.09)(P=0.017), respectively. Conclusion: Our results suggest an intensity-weighted rather than area-based scoring system is more sensitive to detect subtle temporal CT changes in COVID-19, with D-dimer and CRP levels on admission being predictive of the time course of late phase recovery from the disease.

Reverse transcription loop-mediated isothermal amplification combined with nanoparticles-based biosensor for diagnosis of COVID-19 (preprint)

Given the scale and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, known as 2019-nCov) infection (COVID-19), the ongoing global SARS-CoV-2 outbreak has become a huge public health issue. Rapid and precise diagnostic methods are thus immediately needed for diagnosing COVID-19, providing timely treatment and facilitating infection control. A one-step reverse transcription loop-mediated isothermal amplification (RT-LAMP) coupled with nanoparticles-based biosensor (NBS) assay (RT-LAMP-NBS) was successfully established for rapidly and accurately diagnosing COVID-19. A simple equipment (such as heating block) was required for maintaining a constant temperature (63 C) for only 40 min. Using two designed LAMP primer sets, F1ab (opening reading frame 1a/b) and np (nucleoprotein) genes of SARS-CoV-2 were simultaneously amplified and detected in a one-step and single-tube reaction, and the detection results were easily interpreted by NBS. The sensitivity of SARS-CoV-2 RT-LAMP-NBS was 12 copies (each of detection target) per reaction, and no cross-reactivity was generated from non-SARS-CoV-2 templates. Among clinically diagnosed COVID-19 patients, the analytical sensitivity of SARS-CoV-2 was 100% (33/33) in the oropharynx swab samples, and the assay's specificity was also 100% (96/96) when analyzed the clinical samples collected from non-COVID-19 patients. The total diagnosis test from sample collection to result interpretation only takes approximately 1 h. In sum, the RT-LAMP-NBS is a promising tool for diagnosing the current SARS-CoV-2 infection in first line field, public health and clinical laboratories, especially for resource-challenged regions.