RESUMO
The emergence of SARS-CoV-2 variants with mutations in major neutralizing antibody-binding sites can affect humoral immunity induced by infection or vaccination1-6. We analysed the development of anti-SARS-CoV-2 antibody and T cell responses in previously infected (recovered) or uninfected (naive) individuals that received mRNA vaccines to SARS-CoV-2. While previously infected individuals sustained higher antibody titers than uninfected individuals post-vaccination, the latter reached comparable levels of neutralization responses to the ancestral strain than previously infected individuals 7 days after the second vaccine dose. T cell activation markers measured upon spike or nucleocapsid peptide in vitro stimulation showed a progressive increase after vaccination in the time-points analysed. Comprehensive analysis of plasma neutralization using 16 authentic isolates of distinct locally circulating SARS-CoV-2 variants revealed a range of reduction in the neutralization capacity associated with specific mutations in the spike gene: lineages with E484K and N501Y/T (e.g., B.1.351 and P.1) had the greatest reduction, followed by lineages with L452R (e.g., B.1.617.2) or with E484K (without N501Y/T). While both groups retained neutralization capacity against all variants, plasma from previously infected vaccinated individuals displayed overall better neutralization capacity when compared to plasma from uninfected individuals that also received two vaccine doses, pointing to vaccine boosters as a relevant future strategy to alleviate the impact of emerging variants on antibody neutralizing activity.
RESUMO
The emergence and spread of SARS-CoV-2 lineage B.1.1.7, first detected in the United Kingdom, has become a global public health concern because of its increased transmissibility. Over 2500 COVID-19 cases associated with this variant have been detected in the US since December 2020, but the extent of establishment is relatively unknown. Using travel, genomic, and diagnostic data, we highlight the primary ports of entry for B.1.1.7 in the US and locations of possible underreporting of B.1.1.7 cases. Furthermore, we found evidence for many independent B.1.1.7 establishments starting in early December 2020, followed by interstate spread by the end of the month. Finally, we project that B.1.1.7 will be the dominant lineage in many states by mid to late March. Thus, genomic surveillance for B.1.1.7 and other variants urgently needs to be enhanced to better inform the public health response.
RESUMO
There is an urgent need to expand testing for SARS-CoV-2 and other respiratory pathogens as the global community struggles to control the COVID-19 pandemic. Current diagnostic methods can be affected by supply chain bottlenecks and require the assistance of medical professionals, impeding the implementation of large-scale testing. Self-collection of saliva may solve these problems, as it can be completed without specialized training and uses generic materials. In this study, we observed thirty individuals who self-collected saliva using four different collection devices and analyzed their feedback. Two of these devices, a funnel and bulb pipette, were used to evaluate at-home saliva collection by 60 individuals. All devices enabled the safe, unsupervised self-collection of saliva. The quantity and quality of the samples received were acceptable for SARS-CoV-2 diagnostic testing, as determined by RNase P detection. Here, we demonstrate inexpensive, generic, buffer free collection devices suitable for unsupervised and home saliva self-collection.
RESUMO
BackgroundKnowing the true infected and symptomatic case fatality ratios (IFR and CFR) for COVID-19 is of high importance for epidemiological model projections. A large correction factor is usually applied for missed cases. For the United State reported CFR of 5.96%, the estimated IFR values are 10-50-fold lower, justified by early reported CFR values of 0.1% to 0.5% in countries with more extensive testing. However, since then these values have risen 5 to 10-fold. We analyzed their age dependent CFR time courses to explain this increase and to determine whether a common factor can explain their CFRs. MethodsAge dependent time to fatality corrected CFR was calculated using two independent methods. A linear model was developed that predicts CFR based on age dependent CFR coefficients and the age distribution of cases. The model was tested by a linear regression of each countrys CFR against case percentage of 70 years and over. The model was further tested by calculating the percent of the population in New York City who have been infected. ResultsCorrected CFR values ranged from 0.58% to 5.0%. The large majority of CFR variation was explained by case age distribution above 70 years old. Using the CFR derived from the linear model we predicted between 14.7% and 22% of the adult population in NYC had been infected by COVID-19, in agreement with random testing studies (15.3% - 21%). ConclusionsThe large rise in the reported CFR is due to the delay time between infection/diagnosis and fatality with COVID-19. The linear model based on their age specific CFR values provides an alternative method for calculating the true CFR in other regions. Most of the variation in CFR between countries was dependent on case age distribution, which must be considered in measures for mitigating the extensive impacts of the pandemic.
