HMMPolish: a coding region polishing tool for TGS-sequenced RNA viruses.

Access to accurate viral genomes is important to downstream data analysis. Third-generation sequencing (TGS) has recently become a popular platform for virus sequencing because of its long read length. However, its per-base error rate, which is higher than next-generation sequencing, can lead to genomes with errors. Polishing tools are thus needed to correct errors either before or after sequence assembly. Despite promising results of available polishing tools, there is still room to improve the error correction performance to perform more accurate genome assembly. The errors, particularly those in coding regions, can hamper analysis such as linage identification and variant monitoring. In this work, we developed a novel pipeline, HMMPolish, for correcting (polishing) errors in protein-coding regions of known RNA viruses. This tool can be applied to either raw TGS reads or the assembled sequences of the target virus. By utilizing profile Hidden Markov Models of protein families/domains in known viruses, HMMPolish can correct errors that are ignored by available polishers. We extensively validated HMMPolish on 34 datasets that covered four clinically important viruses, including HIV-1, influenza-A, norovirus, and severe acute respiratory syndrome coronavirus 2. These datasets contain reads with different properties, such as sequencing depth and platforms (PacBio or Nanopore). The benchmark results against popular/representative polishers show that HMMPolish competes favorably on error correction in coding regions of known RNA viruses.

Risk Factors for Slow Viral Decline in COVID-19 Patients during the 2022 Omicron Wave.

Formulating termination of isolation (de-isolation) policies requires up-to-date knowledge about viral shedding dynamics. However, current de-isolation policies are largely based on viral load data obtained before the emergence of Omicron variant. In this retrospective cohort study involving adult patients hospitalised for COVID-19 between January and February 2022, we sought to determine SARS-CoV-2 viral shedding kinetics and to investigate the risk factors associated with slow viral decline during the 2022 Omicron wave. A total of 104 patients were included. The viral load was highest (Ct value was lowest) on days 1 post-symptom-onset (PSO) and gradually declined. Older age, hypertension, hyperlipidaemia and chronic kidney disease were associated with slow viral decline in the univariate analysis on both day 7 and day 10 PSO, while incomplete or no vaccination was associated with slow viral decline on day 7 PSO only. However, older age was the only risk factor that remained statistically significant in the multivariate analysis. In conclusion, older age is an independent risk factor associated with slow viral decline in this study conducted during the Omicron-dominant 2022 COVID-19 wave. Transmission-based precaution guidelines should take age into consideration when determining the timing of de-isolation.

Contribution of low population immunity to the severe Omicron BA.2 outbreak in Hong Kong.

Monitoring population protective immunity against SARS-CoV-2 variants is critical for risk assessment. We hypothesize that Hong Kong's explosive Omicron BA.2 outbreak in early 2022 could be explained by low herd immunity. Our seroprevalence study using sera collected from January to December 2021 shows a very low prevalence of neutralizing antibodies (NAb) against ancestral virus among older adults. The age group-specific prevalence of NAb generally correlates with the vaccination uptake rate, but older adults have a much lower NAb seropositive rate than vaccination uptake rate. For all age groups, the seroprevalence of NAb against Omicron variant is much lower than that against the ancestral virus. Our study suggests that this BA.2 outbreak and the exceptionally high case-fatality rate in the ≥80 year-old age group (9.2%) could be attributed to the lack of protective immunity in the population, especially among the vulnerable older adults, and that ongoing sero-surveillance is essential.

Boosting of serum neutralizing activity against the Omicron variant among recovered COVID-19 patients by BNT162b2 and CoronaVac vaccines.

BACKGROUND: SARS-CoV-2 Omicron variant evades immunity from past infection or vaccination and is associated with a greater risk of reinfection among recovered COVID-19 patients. We assessed the serum neutralizing antibody (NAb) activity against Omicron variant (Omicron NAb) among recovered COVID-19 patients with or without vaccination. METHODS: In this prospective cohort study with 135 recovered COVID-19 patients, we determined the serum NAb titers against ancestral virus or variants using a live virus NAb assay. We used the receiver operating characteristic analysis to determine the optimal cutoff for a commercially-available surrogate NAb assay. FINDINGS: Among recovered COVID-19 patients, the serum live virus geometric mean Omicron NAb titer was statistically significantly higher among BNT162b2 recipients compared to non-vaccinated individuals (85.4 vs 5.6,P < 0.0001). The Omicron seropositive rates in live virus NAb test (NAb titer ≥10) were statistically significantly higher among BNT162b2 (90.6% [29/32];P < 0.0001) or CoronaVac (36.7% [11/30]; P = 0.0115) recipients when compared with non-vaccinated individuals (12.3% [9/73]). Subgroup analysis of CoronaVac recipients showed that the Omicron seropositive rates were higher among individuals with two doses than those with one dose (85.7% vs 21.7%; P = 0.0045). For the surrogate NAb assay, a cutoff of 109.1 AU/ml, which is 7.3-fold higher than the manufacturer's recommended cutoff, could achieve a sensitivity and specificity of 89.5% and 89.8%, respectively, in detecting Omicron NAb. INTERPRETATION: Among individuals with prior COVID-19, one dose of BNT162b2 or two doses of CoronaVac could induce detectable serum Omicron NAb. Our result would be particularly important for guiding vaccine policies in countries with COVID-19 vaccine shortage. FUNDING: Health and Medical Research Fund, Richard and Carol Yu, Michael Tong (see acknowledgments for full list).

Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron Subvariant BA.2 in a Single-Source Community Outbreak.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant BA.2 sublineage has increased rapidly in Europe and Asia since January 2022. Here, we report the epidemiological and genomic analysis of a large single-source BA.2 outbreak in a housing estate. METHODS: We analyzed the epidemiological information on a community outbreak of BA.2 (STY outbreak). We performed whole viral genome sequencing using the Oxford Nanopore MinION device. We calculated the doubling time of the outbreak within a housing estate. RESULTS: The STY outbreak involved a total of 768 individuals as of 5 February 2022, including 432 residents, visitors, or staff (56.3%) from a single housing estate (KC Estate). The outbreak at the KC Estate had a short doubling time of 1.28 days (95% confidence interval: .560-1.935). The outbreak was promptly controlled with the lockdown of 3 buildings within the housing estate. Whole-genome sequencing was performed for 133 patients in the STY outbreak, including 106 residents of the KC Estate. All 133 sequences from the STY outbreak belonged to the BA.2 sublineage, and phylogenetic analysis showed that these sequences cluster together. All individuals in the STY cluster had the unique mutation C12525T. CONCLUSIONS: Our study highlights the exceptionally high transmissibility of the Omicron variant BA.2 sublineage in Hong Kong, where stringent measures are implemented as part of the elimination strategy. Continual genomic surveillance is crucial in monitoring the emergence of epidemiologically important Omicron sublineages.

Probable Animal-to-Human Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Delta Variant AY.127 Causing a Pet Shop-Related Coronavirus Disease 2019 (COVID-19) Outbreak in Hong Kong.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can infect human and other mammals, including hamsters. Syrian (Mesocricetus auratus) and dwarf (Phodopus sp.) hamsters are susceptible to SARS-CoV-2 infection in the laboratory setting. However, pet shop-related Coronavirus Disease 2019 (COVID-19) outbreaks have not been reported. METHODS: We conducted an investigation of a pet shop-related COVID-19 outbreak due to Delta variant AY.127 involving at least 3 patients in Hong Kong. We tested samples collected from the patients, environment, and hamsters linked to this outbreak and performed whole genome sequencing analysis of the reverse transcription polymerase chain reaction (RT-PCR)-positive samples. RESULTS: The patients included a pet shop keeper (Patient 1), a female customer of the pet shop (Patient 2), and the husband of Patient 2 (Patient 3). Investigation showed that 17.2% (5/29) and 25.5% (13/51) environmental specimens collected from the pet shop and its related warehouse, respectively, tested positive for SARS-CoV-2 RNA by RT-PCR. Among euthanized hamsters randomly collected from the storehouse, 3% (3/100) tested positive for SARS-CoV-2 RNA by RT-PCR and seropositive for anti-SARS-CoV-2 antibody by enzyme immunoassay. Whole genome analysis showed that although all genomes from the outbreak belonged to the Delta variant AY.127, there were at least 3 nucleotide differences among the genomes from different patients and the hamster cages. Genomic analysis suggests that multiple strains have emerged within the hamster population, and these different strains have likely transmitted to human either via direct contact or via the environment. CONCLUSIONS: Our study demonstrated probable hamster-to-human transmission of SARS-CoV-2. As pet trading is common around the world, this can represent a route of international spread of this pandemic virus.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.