Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh.

Background: Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection. Methodology: In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models. Result: The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection. Conclusion: Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

Covishield vaccine induces robust immune responses in Bangladeshi adults.

Objective: To evaluate severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-specific antibody responses after Covishield vaccination for 6 months after vaccination. Design: SARS-CoV-2-specific antibody responses were assessed by enzyme-linked immunosorbent assay of the recombinant receptor-binding domain of SARS-CoV-2 in 381 adults given the Covishield vaccine at baseline (n=119), 1 month (n=126) and 2 months (n=75) after the first dose, 1 month after the second dose (n=161), and monthly for 3 additional months. Results: Over 51% of participants were seropositive at baseline (before vaccination with Covishield), and almost all participants (159/161) became seropositive 1 month after the second dose. Antibody levels peaked 1 month after receipt of the second dose of vaccine, and decreased by 4 months after the first dose; the lowest responses were found 6 months after the first dose, although antibody responses and responder frequencies remained significantly higher compared with baseline (P<0.0001). Compared with younger participants, older participants had lower antibody responses 6 months after the first dose of vaccine (P<0.05). Participants who had previous SARS-CoV-2 infection showed robust higher antibody responses after vaccination. Conclusions: These findings help to elucidate the longevity of vaccine-specific antibody responses following vaccination with Covishield, and provide information relevant to the planning of booster doses after the initial two doses of vaccine.

Seroprevalence of SARS-CoV-2 antibodies in Bangladesh related to novel coronavirus infection.

Design: A cross-sectional study was conducted amongst household members in 32 districts of Bangladesh to build knowledge about disease epidemiology and seroepidemiology of coronavirus disease 2019 (COVID-19). Objective: Antibody responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) were assessed in people between April and October 2020. Results: The national seroprevalence rates of immunoglobulin G (IgG) and IgM were estimated to be 30.4% and 39.7%, respectively. In Dhaka, the seroprevalence of IgG was 35.4% in non-slum areas and 63.5% in slum areas. In areas outside of Dhaka, the seroprevalence of IgG was 37.5% in urban areas and 28.7% in rural areas. Between April and October 2020, the highest seroprevalence rate (57% for IgG and 64% for IgM) was observed in August. IgM antibody was more prevalent in younger participants, while older participants had more frequent IgG seropositivity. Follow-up specimens from patients with COVID-19 and their household members suggested that both IgG and IgM seropositivity increased significantly at day 14 and day 28 compared with day 1 after enrolment. Conclusions: SARS-CoV-2 had spread extensively in Bangladesh by October 2020. This highlights the importance of monitoring seroprevalence data, particularly with the emergence of new SARS-CoV-2 variants over time.

Disease characteristics and serological responses in patients with differing severity of COVID-19 infection: A longitudinal cohort study in Dhaka, Bangladesh.

BACKGROUND: COVID-19 caused by SARS-CoV-2 ranges from asymptomatic to severe disease and can cause fatal and devastating outcome in many cases. In this study, we have compared the clinical, biochemical and immunological parameters across the different disease spectrum of COVID-19 in Bangladeshi patients. METHODOLOGY/PRINCIPAL FINDINGS: This longitudinal study was conducted in two COVID-19 hospitals and also around the community in Dhaka city in Bangladesh between November 2020 to March 2021. A total of 100 patients with COVID-19 infection were enrolled and classified into asymptomatic, mild, moderate and severe cases (n = 25/group). In addition, thirty age and sex matched healthy participants were enrolled and 21 were analyzed as controls based on exclusion criteria. After enrollment (study day1), follow-up visits were conducted on day 7, 14 and 28 for the cases. Older age, male gender and co-morbid conditions were the risk factors for severe COVID-19 disease. Those with moderate and severe cases of infection had low lymphocyte counts, high neutrophil counts along with a higher neutrophil-lymphocyte ratio (NLR) at enrollment; this decreased to normal range within 42 days after the onset of symptom. At enrollment, D-dimer, CRP and ferritin levels were elevated among moderate and severe cases. The mild, moderate, and severe cases were seropositive for IgG antibody by day 14 after enrollment. Moderate and severe cases showed significantly higher IgM and IgG levels of antibodies to SARS-CoV-2 compared to mild and asymptomatic cases. CONCLUSION/SIGNIFICANCE: We report on the clinical, biochemical, and hematological parameters associated with the different severity of COVID-19 infection. We also show different profile of antibody response against SARS-CoV-2 in relation to disease severity, especially in those with moderate and severe disease manifestations compared to the mild and asymptomatic infection.

Genomics, social media and mobile phone data enable mapping of SARS-CoV-2 lineages to inform health policy in Bangladesh.

Genomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook 'Data for Good' and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.

Genome Sequences of 15 SARS-CoV-2 Sublineage B.1.617.2 Strains in Bangladesh.

We report the coding-complete genome sequences of 15 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sublineage B.1.617.2 strains that were obtained from Bangladeshi individuals with a history of recent travel to India and from the Bangladeshi community. Genomic data were generated by Nanopore sequencing using the amplicon sequencing approach developed by the ARTIC Network.