A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples

Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the utility of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing 'gold standard' dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected a reasonable number of change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop 'gold standard' dates about the onset of outbreaks due to new variants.

Shift in Demographic Involvement and Clinical Characteristics of COVID-19 From Wild-Type SARS-CoV-2 to the Delta Variant in the Indian Population: In Silico Analysis

BACKGROUND

The Delta variant (B.1.617.2) was considered the most dangerous SARS-CoV-2 strain; however, in-depth studies on its impact based on demographic and clinical characteristics of COVID-19 are scarce.

OBJECTIVE

We aimed to investigate the shift in demographic and clinical characteristics of the COVID-19 pandemic with the emergence of the SARS-CoV-2 Delta variant compared with the wild-type (WT) strain (B.1).

METHODS

A cross-sectional study of COVID-19 cases in the Indian population caused by the WT strain (B.1) and Delta variant of SARS-CoV-2 was performed. The viral genomic sequence metadata containing demographic, vaccination, and patient status details (N=9500, NDelta=6238, NWT=3262) were statistically analyzed.

RESULTS

With the Delta variant, in comparison with the WT strain, a higher proportion of young individuals (<20 years) were infected (0-9 years: Delta: 281/6238, 4.5% vs B.1: 75/3262, 2.3%; 10-19 years: Delta: 562/6238, 9% vs B.1: 229/3262, 7%; P<.001). The proportion of women contracting infection increased (Delta: 2557/6238, 41% vs B.1: 1174/3262, 36%; P<.001). However, it decreased for men (Delta: 3681/6238, 59% vs B.1: 2088/3262, 64%; P<.001). An increased proportion of the young population developed symptomatic illness and were hospitalized (Delta: 27/262, 10.3% vs B.1: 5/130, 3.8%; P=.02). Moreover, an increased proportion of the women (albeit not men) from the young (Delta: 37/262, 14.1% vs B.1: 4/130, 3.1%; P<.001) and adult (Delta: 197/262, 75.2% vs B.1: 72/130, 55.4%; P<.001) groups developed symptomatic illness and were hospitalized. The mean age of men and women who contracted infection (Delta: men=37.9, SD 17.2 years; women=36.6, SD 17.6 years; P<.001; B.1: men=39.6, SD 16.9 years; women=40.1, SD 17.4 years; P<.001) as well as developing symptoms or being hospitalized (Delta: men=39.6, SD 17.4 years; women=35.6, SD 16.9 years, P<.001; B.1: men=47, SD 18 years; women=49.5, SD 20.9 years, P<.001) were considerably lower with the Delta variant than the B.1 strain. The total mortality was about 1.8 times higher with the Delta variant than with the WT strain. With the Delta variant, compared with B.1, mortality decreased for men (Delta: 58/85, 68% vs B.1: 15/20, 75%; P<.001); in contrast, it increased for women (Delta: 27/85, 32% vs B.1: 5/20, 25%; P<.001). The odds of death increased with age, irrespective of sex (odds ratio 3.034, 95% CI 1.7-5.2, P<.001). Frequent postvaccination infections (24/6238) occurred with the Delta variant following complete doses.

CONCLUSIONS

The increased involvement of young people and women, the lower mean age for illness, higher mortality, and frequent postvaccination infections were significant epidemiological concerns with the Delta variant.

Beyond the waves: Unraveling pandemic outcomes with genomic insights and immunity analysis - Evidence from 14 countries

OBJECTIVE

Although the World Health Organization and many governments have recategorized COVID-19 as a generally mild to moderately severe disease, consecutive pandemic waves driven by immune escape variants have underscored the need for timely and accurate prediction of the next outbreak. Nevertheless, little attention has been paid to translating genomic data and infection- and vaccine-induced immunity into direct estimates.

METHODS

We retrieved epidemiologic and genomic data shortly before pandemic waves across 14 developed countries from late 2021 to mid-2022 and examined associations between early-stage variant competition, infection- and vaccine-induced immunity, and the time intervals between wave peaks. We applied regression analysis and the generalized estimating equation method to construct an inferential model.

RESULTS

Each per cent increase in the proportion of a new variant was associated with a 1.0% reduction in interpeak intervals on average. Curvilinear associations between vaccine-induced immunity and outcome variables were observed, suggesting that reaching a critical vaccine distribution rate may decrease the caseload of the upcoming wave.

CONCLUSIONS

By leveraging readily accessible pre-outbreak genomic and epidemiologic data, our results not only substantiate the predictive potential of early variant fractions but also propose that immunity acquired through infection alone may not sufficiently mitigate transmission. Conversely, a rapid and widespread vaccination initiative appears to be correlated with a decrease in disease incidence.

Psychiatric Symptoms and Fatigue in COVID-19 Survivors

INTRODUCTION

Psychiatric symptoms and fatigue are common after the coronavirus disease 2019 (COVID-19) illness. The cause of these symptoms is direct neuronal injury and indirect injury with immune-mediated inflammation. In addition, social factors also affect mental health.

OBJECTIVE

We aim to compare psychiatric symptoms and fatigue between COVID-19 survivors and healthy controls.

MATERIAL AND METHODS

We prospectively evaluated 100 COVID-19 survivors for anxiety, depression, positive affect, and behavior control using the Mental Health Inventory (MHI). Fatigue is assessed using the Modified Fatigue Impact Scale (MFIS) score. We compared them with 100 healthy controls.

RESULTS

There was a significant statistical difference between the MHI score and individual components of MHI. Overall, MHI scores in cases and controls were 79.41 and 93.31, respectively, with a P value of less than 0.0001. Computed scores for anxiety, depression, behavior control, and positive affect of COVID-19 survivors showed statistically significant differences as compared to healthy controls. There was a weak association between hospital stay duration and poor MHI scores. Fatigue was significantly worse in COVID-19 survivors, with a mean score of 6.93 in cases and 5.35 in controls, with a P value of 0.0001. This was a cross-sectional study evaluating psychiatric symptom scores, but not establishing the diagnosis. It is suggested that appropriate treatment and counseling for these symptoms should be done.

CONCLUSIONS

Psychiatric symptoms and fatigue were significantly more common in COVID-19 patients after recovery from acute illness. It is a major contributing cause of morbidity other than organic complications of COVID-19 and requires attention in management.

SARS-CoV-2 Omicron Variant Genomic Sequences and Their Epidemiological Correlates Regarding the End of the Pandemic: In Silico Analysis

BACKGROUND

Emergence of the new SARS-CoV-2 variant B.1.1.529 worried health policy makers worldwide due to a large number of mutations in its genomic sequence, especially in the spike protein region. The World Health Organization (WHO) designated this variant as a global variant of concern (VOC), which was named "Omicron." Following Omicron's emergence, a surge of new COVID-19 cases was reported globally, primarily in South Africa.

OBJECTIVE

The aim of this study was to understand whether Omicron had an epidemiological advantage over existing variants.

METHODS

We performed an in silico analysis of the complete genomic sequences of Omicron available on the Global Initiative on Sharing Avian Influenza Data (GISAID) database to analyze the functional impact of the mutations present in this variant on virus-host interactions in terms of viral transmissibility, virulence/lethality, and immune escape. In addition, we performed a correlation analysis of the relative proportion of the genomic sequences of specific SARS-CoV-2 variants (in the period from October 1 to November 29, 2021) with matched epidemiological data (new COVID-19 cases and deaths) from South Africa.

RESULTS

Compared with the current list of global VOCs/variants of interest (VOIs), as per the WHO, Omicron bears more sequence variation, specifically in the spike protein and host receptor-binding motif (RBM). Omicron showed the closest nucleotide and protein sequence homology with the Alpha variant for the complete sequence and the RBM. The mutations were found to be primarily condensed in the spike region (n=28-48) of the virus. Further mutational analysis showed enrichment for the mutations decreasing binding affinity to angiotensin-converting enzyme 2 receptor and receptor-binding domain protein expression, and for increasing the propensity of immune escape. An inverse correlation of Omicron with the Delta variant was noted (r=-0.99, P<.001; 95% CI -0.99 to -0.97) in the sequences reported from South Africa postemergence of the new variant, subsequently showing a decrease. There was a steep rise in new COVID-19 cases in parallel with the increase in the proportion of Omicron isolates since the report of the first case (74%-100%). By contrast, the incidence of new deaths did not increase (r=-0.04, P>.05; 95% CI -0.52 to 0.58).

CONCLUSIONS

In silico analysis of viral genomic sequences suggests that the Omicron variant has more remarkable immune-escape ability than existing VOCs/VOIs, including Delta, but reduced virulence/lethality than other reported variants. The higher power for immune escape for Omicron was a likely reason for the resurgence in COVID-19 cases and its rapid rise as the globally dominant strain. Being more infectious but less lethal than the existing variants, Omicron could have plausibly led to widespread unnoticed new, repeated, and vaccine breakthrough infections, raising the population-level immunity barrier against the emergence of new lethal variants. The Omicron variant could have thus paved the way for the end of the pandemic.

Mutational Patterns Observed in SARS-CoV-2 Genomes Sampled From Successive Epochs Delimited by Major Public Health Events in Ontario, Canada: Genomic Surveillance Study

BACKGROUND

The emergence of SARS-CoV-2 variants with mutations associated with increased transmissibility and virulence is a public health concern in Ontario, Canada. Characterizing how the mutational patterns of the SARS-CoV-2 genome have changed over time can shed light on the driving factors, including selection for increased fitness and host immune response, that may contribute to the emergence of novel variants. Moreover, the study of SARS-CoV-2 in the microcosm of Ontario, Canada can reveal how different province-specific public health policies over time may be associated with observed mutational patterns as a model system.

OBJECTIVE

This study aimed to perform a comprehensive analysis of single base substitution (SBS) types, counts, and genomic locations observed in SARS-CoV-2 genomic sequences sampled in Ontario, Canada. Comparisons of mutational patterns were conducted between sequences sampled during 4 different epochs delimited by major public health events to track the evolution of the SARS-CoV-2 mutational landscape over 2 years.

METHODS

In total, 24,244 SARS-CoV-2 genomic sequences and associated metadata sampled in Ontario, Canada from January 1, 2020, to December 31, 2021, were retrieved from the Global Initiative on Sharing All Influenza Data database. Sequences were assigned to 4 epochs delimited by major public health events based on the sampling date. SBSs from each SARS-CoV-2 sequence were identified relative to the MN996528.1 reference genome. Catalogues of SBS types and counts were generated to estimate the impact of selection in each open reading frame, and identify mutation clusters. The estimation of mutational fitness over time was performed using the Augur pipeline.

RESULTS

The biases in SBS types and proportions observed support previous reports of host antiviral defense activity involving the SARS-CoV-2 genome. There was an increase in U>C substitutions associated with adenosine deaminase acting on RNA (ADAR) activity uniquely observed during Epoch 4. The burden of novel SBSs observed in SARS-CoV-2 genomic sequences was the greatest in Epoch 2 (median 5), followed by Epoch 3 (median 4). Clusters of SBSs were observed in the spike protein open reading frame, ORF1a, and ORF3a. The high proportion of nonsynonymous SBSs and increasing dN/dS metric (ratio of nonsynonymous to synonymous mutations in a given open reading frame) to above 1 in Epoch 4 indicate positive selection of the spike protein open reading frame.

CONCLUSIONS

Quantitative analysis of the mutational patterns of the SARS-CoV-2 genome in the microcosm of Ontario, Canada within early consecutive epochs of the pandemic tracked the mutational dynamics in the context of public health events that instigate significant shifts in selection and mutagenesis. Continued genomic surveillance of emergent variants will be useful for the design of public health policies in response to the evolving COVID-19 pandemic.