Neonatal mortality is affected by socioeconomic, community level and proximate biological determinants.

India has witnessed a devastating second wave of COVID-19, which peaked during the last week of April and the second week of May, 2021. We aimed to understand whether the arrival of second wave was predictable and whether it was driven by the existing SARS-CoV-2 strains or any of the emerging variants. We analyzed the monthly distribution of the genomic sequence data for SARS-CoV-2 from India and correlated that with the epidemiological data for new cases and deaths, for the corresponding period of the second wave. Our analysis shows that the first indications of arrival of the second wave were observable by January, 2021, and by March, 2021 it was clearly predictable. B.1.617 lineage variants drove the wave, particularly B.1.617.2 (a.k.a. delta variant). We propose that genomic surveillance of the SARS-CoV-2 variants augmented with epidemiological data can be a promising tool for predicting future COVID-19 waves.

Background: A newly emerged SARS-CoV-2 variant B.1.1.529 has worried health policymakers worldwide due to the presence of a large number of mutations in its genomic sequence, especially in the spike protein region. World Health Organization (WHO) has designated it as a global variant of concern (VOC) and has named as Omicron. A surge in new COVID-19 cases has been reported from certain geographical locations, primarily in South Africa (SA) following the emergence of Omicron. Materials and methods: We performed an in silico analysis of the complete genomic sequences of Omicron available on GISAID (until 2021-12-6) to predict 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 of 01 Oct-29 Nov 2021) with the current epidemiological data (new COVID-19 cases and deaths) from SA to understand whether the Omicron has an epidemiological advantage over existing variants. Results: Compared to the current list of global VOCs/VOIs (as per 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 Alpha variant for the complete sequence as well as for RBM. The mutations were found primarily condensed in the spike region (28-48) of the virus. Further, the mutational analysis showed enrichment for the mutations decreasing ACE2-binding affinity and RBD protein expression, in contrast, increasing the propensity of immune escape. An inverse correlation of Omicron with Delta variant was noted (r=-0.99, p< .001, 95% CI: -0.99 to -0.97) in the sequences reported from SA post-emergence of the new variant, later showing a decrease. There has been a steep rise in the new COVID-19 cases in parallel with the increase in the proportion of Omicron since the first case (74-100%), on the contrary, the incidences of new deaths have not been increased (r=-0.04, p>0.05, 95% CI =-0.52 to 0.58). Conclusions: Omicron may have greater immune escape ability than the existing VOCs/VOIs. However, there are no clear indications coming out from the predictive mutational analysis that the Omicron may have higher virulence/lethality than other variants, including Delta. The higher ability for immune escape may be a likely reason for the recent surge in Omicron cases in SA.

Importance: Higher risks of contracting infection, developing severe illness and mortality are known facts in aged and male sex if exposed to the wild type SARS-CoV-2 strains (Wuhan and B.1 strains). Now, accumulating evidence suggests greater involvement of lower age and narrowing the age and sex based differences for the severity of symptoms in infections with emerging SARS-CoV-2 variants. Delta variant (B.1.617.2) is now a globally dominant SARS-CoV-2 strain, however, current evidence on demographic characteristics for this variant are limited. Recently, delta variant caused a devastating second wave of COVID-19 in India. We performed a demographic characterization of COVID-19 cases in Indian population diagnosed with SARS-CoV-2 genomic sequencing for delta variant. Objective: To determine demographic characteristics of delta variant in terms of age and sex, severity of the illness and mortality rate, and post-vaccination infections. Design: A cross sectional study Setting: Demographic characteristics, including vaccination status (for two complete doses) and severity of the illness and mortality rate, of COVID-19 cases caused by wild type strain (B.1) and delta variant (B.1.617.2) of SARS-CoV-2 in Indian population were studied. Participants: COVID-19 cases for which SARS-CoV-2 genomic sequencing was performed and complete demographic details (age, sex, and location) were available, were included. Exposures: SARS-CoV-2 infection with Delta (B.1.617.2) variant and wild type (B.1) strain. Main Outcomes and Measures: The patient metadata containing details for demographic and vaccination status (two complete doses) of the COVID-19 patients with confirmed delta variant and WT (B.1) infections were analyzed [total number of cases (N) =9500, NDelta =6238, NWT=3262]. Further, severity of the illness and mortality were assessed in subsets of patients. Final data were tabulated and statistically analyzed to determine age and sex based differences in chances of getting infection and the severity of illness, and post-vaccination infections were compared between wild type and delta variant strains. Graphs were plotted to visualize the trends. Results: With delta variant, in comparison to wild type (B.1) strain, higher proportion of young age individuals (<20 year) (0-9 year: 4.47% vs. 2.3%, 10-19 year: 9% vs. 7%) were affected. The proportion of women contracting infection were increased (41% vs. 36%). The higher proportion of total young (0-19 year, 10% vs. 4%) (p=.017) population and young (14% vs. 3%) as well as adult (20-59 year, 75% vs. 55%) women developed symptoms/hospitalized with delta variant in comparison to B.1 infection (p< .00001). The mean age of contracting infection [Delta, men=37.9 (17.2) year, women=36.6 (17.6) year; B.1, men=39.6 (16.9) year and women= 40.1 (17.4) year (p<.001)] as well as developing symptoms/hospitalization [Delta, men=39.6(17.4) year, women=35.6(16.9) year; B.1, men=47(18) year and women= 49.5(20.9) year (p<.001)] was considerably lower. The total mortality was about 1.8 times higher (13% vs. 7%). Risk of death increased irrespective of the sex (Odds ratio: 3.034, 95% Confidence Interval: 1.7-5.2, p<0.001), however, increased proportion of women (32% vs. 25%) were demised. Further, multiple incidences of delta infections were noted following complete vaccination. Conclusions and Relevance: The increased involvement of young (0-19 year) and women, lower mean age for contracting infection and symptomatic illness/hospitalization, higher mortality, and frequent incidences of post-vaccination infections with delta variant compared to wild type strain raises significant epidemiological concerns.

Background Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model. Objective We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave. Methods We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period. Results Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant). Conclusions Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.

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.

Conclusion Respiratory and gastrointestinal diseases and accidents were predominant causes of hospitalisation of children under 2 years of age. The results demonstrate that prevention activities could be essential strategies in order to reduce proportions of hospitalisation among children under 2 years of age in Brazil at least by half. Besides, this study suggests that such investment in health promotion could improve the health profiles of children in developing countries.

UNSTRUCTURED India has witnessed a devastating second wave of COVID-19, which peaked during the last week of April and the second week of May, 2021. We aimed to understand whether the arrival of second wave was predictable and whether it was driven by the existing SARS-CoV-2 strains or any of the emerging variants. We analyzed the monthly distribution of the genomic sequence data for SARS-CoV-2 from India and correlated that with the epidemiological data for new cases and deaths, for the corresponding period of the second wave. Our analysis shows that the first indications of arrival of the second wave were observable by January, 2021, and by March, 2021 it was clearly predictable. B.1.617 lineage variants drove the wave, particularly B.1.617.2 (a.k.a. delta variant). We propose that genomic surveillance of the SARS-CoV-2 variants augmented with epidemiological data can be a promising tool for predicting future COVID-19 waves.