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[...]immunity against infection might have already begun to wane by December, 2020, because of a general decrease in immune protection against SARS-CoV-2 after a first exposure. [...]SARS-CoV-2 lineages might evade immunity generated in response to previous infection.15 Three recently detected SARS-CoV-2 lineages (B.1.1.7, B.1.351, and P.1), are unusually divergent and each possesses a unique constellation of mutations of potential biological importance.16–18 Of these, two are circulating in Brazil (B.1.1.7 and P.1) and one (P.1) was detected in Manaus on Jan 12, 2021.16 One case of SARS-CoV-2 reinfection has been associated with the P.1 lineage in Manaus19 that accrued ten unique spike protein mutations, including E484K and N501K.16 Moreover, the newly classified P.2 lineage (sublineage of B.1.128 that independently accrued the spike E484K mutation) has now been detected in several locations in Brazil, including Manaus.20 P.2 variants with the E484K mutation have been detected in two people who have been reinfected with SARS-CoV-2 in Brazil,21,22 and there is in-vitro evidence that the presence of the E484K mutation reduces neutralisation by polyclonal antibodies in convalescent sera.15 Fourth, SARS-CoV-2 lineages circulating in the second wave might have higher inherent transmissibility than pre-existing lineages circulating in Manaus. The protocols and findings of such studies should be coordinated and rapidly shared wherever such variants emerge and spread. Since rapid data sharing is the basis for the development and implementation of actionable disease control measures during public health emergencies, we are openly sharing in real-time monthly curated serosurvey data from blood donors through the Brazil–UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE) Centre GitHub website and will continue to share genetic sequence data and results from Manaus through openly accessible data platforms such as GISAID and Virological.

Most longitudinal studies of COVID-19 incidence have used unlinked samples. The city of Manaus, Brazil, has a blood donation program which allows sample linkage, and was struck by two large COVID-19 epidemic waves between mid-2020 and early 2021. We estimated the changing force of infection, i.e. incidence in susceptible individuals. Seroconversion was inferred by a mixture model for serial values from the Abbott Architect SARS-CoV-2 nucleocapsid (N) IgG assay. We estimated the number of suspected COVID-19 hospitalizations arising from each infection over calendar time. Whole blood donations between April 2020 and March 2021 were included from 6734 people, 2747 with two or more donations. The inferred criterion for seroconversion, and thus an incident infection, was a 6.07 fold increase in N IgG reactivity. The overall force of infection was 1.19 per person year (95% confidence interval 1.14-1.24) during the two main waves. The estimated number of suspected hospitalizations per infection, was approximately 4.1 times higher in the second wave than in the first. Serial values from this assay can be used to infer seroconversion over time, and in Manaus show a higher number of suspected COVID-19 hospitalizations per infection in the second wave relative to the first.

IntroductionLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities.MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities.ResultsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45).ConclusionsLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.

ObjectiveDose shortages delayed access to COVID-19 vaccination. We aim to characterise inequality in two-dose vaccination by sociodemographic group across Brazil.DesignThis is a cross-sectional study.SettingWe used data retrieved from the Brazilian Ministry of Health databases published between 17 January 2021 and 6 September 2021.MethodsWe assessed geographical inequalities in full vaccination coverage and dose by age, sex, race and socioeconomic status. We developed a Campaign Optimality Index to characterise inequality in vaccination access due to premature vaccination towards younger populations before older and vulnerable populations were fully vaccinated. Generalised linear regression was used to investigate the risk of death and hospitalisation by age group, socioeconomic status and vaccination coverage.ResultsVaccination coverage is higher in the wealthier South and Southeast. Men, people of colour and low-income groups were more likely to be only partially vaccinated due to missing or delaying a second dose. Vaccination started prematurely for age groups under 50 years which may have hindered uptake in older age groups. Vaccination coverage was associated with a lower risk of death, especially in older age groups (ORs 9.7 to 29.0, 95% CI 9. 4 to 29.9). Risk of hospitalisation was greater in areas with higher vaccination rates due to higher access to care and reporting.ConclusionsVaccination inequality persists between states, age and demographic groups despite increasing uptake. The association between hospitalisation rates and vaccination is attributed to preferential delivery to areas of greater transmission and access to healthcare.

Accurate parameters are crucial in modern energy systems to ensure the reliable operation of all components. Given the substantial volume of data in monitored systems, high-performance methods are necessary. This paper proposes a new Bayesian multi-output regressor for estimating the parameters of a three-phase transmission line. The presented approach achieves acceptable accuracy in parameter estimation using only one end of the line. The Bayesian regressor is developed using information derived from the data themselves, eliminating the need to explicitly model the system. This capability allows the method to estimate parameters while accommodating different noise models, even in the presence of systematic errors and non-Gaussian random noise. The methodology was validated on various systems, including a two-bus system, IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus, under diverse conditions such as varying sample sizes, loads, and noise levels. These tests demonstrate the robustness of the proposed approach.

Background The city of Manaus, north Brazil, was stricken by a second epidemic wave of SARS-CoV-2 despite high seroprevalence estimates, coinciding with the emergence of the Gamma (P.1) variant. Reinfections were postulated as a partial explanation for the second surge. However, accurate calculation of reinfection rates is difficult when stringent criteria as two time-separated RT-PCR tests and/or genome sequencing are required. To estimate the proportion of reinfections caused by Gamma during the second wave in Manaus and the protection conferred by previous infection, we identified anti-SARS-CoV-2 antibody boosting in repeat blood donors as a mean to infer reinfection. Methods We tested serial blood samples from unvaccinated repeat blood donors in Manaus for the presence of anti-SARS-CoV-2 IgG antibodies using two assays that display waning in early convalescence, enabling the detection of reinfection-induced boosting. Donors were required to have three or more donations, being at least one during each epidemic wave. We propose a strict serological definition of reinfection (reactivity boosting following waning like a V-shaped curve in both assays or three spaced boostings), probable (two separate boosting events) and possible (reinfection detected by only one assay) reinfections. The serial samples were used to divide donors into six groups defined based on the inferred sequence of infection and reinfection with non-Gamma and Gamma variants. Results From 3655 repeat blood donors, 238 met all inclusion criteria, and 223 had enough residual sample volume to perform both serological assays. We found 13.6% (95% CI 7.0–24.5%) of all presumed Gamma infections that were observed in 2021 were reinfections. If we also include cases of probable or possible reinfections, these percentages increase respectively to 22.7% (95% CI 14.3–34.2%) and 39.3% (95% CI 29.5–50.0%). Previous infection conferred a protection against reinfection of 85.3% (95% CI 71.3–92.7%), decreasing to respectively 72.5% (95% CI 54.7–83.6%) and 39.5% (95% CI 14.1–57.8%) if probable and possible reinfections are included. Conclusions Reinfection by Gamma is common and may play a significant role in epidemics where Gamma is prevalent, highlighting the continued threat variants of concern pose even to settings previously hit by substantial epidemics.

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Dengue fever is re-emerging worldwide, however the reasons of this new emergence are not fully understood. Our goal was to report the incidence of dengue in one of the most populous States of Brazil, and to assess the high-risk areas using a spatial and spatio-temporal annual models including geoclimatic, demographic and socioeconomic characteristics. An ecological study with both, a spatial and a temporal component was carried out in Sao Paulo State, Southeastern Brazil, between January 1st, 2007 and December 31st, 2019. Crude and Bayesian empirical rates of dengue cases following by Standardized Incidence Ratios (SIR) were calculated considering the municipalities as the analytical units and using the Integrated Nested Laplace Approximation in a Bayesian context. A total of 2,027,142 cases of dengue were reported during the studied period. The spatial model allocated the municipalities in four groups according to the SIR values: (I) SIR<0.8; (II) SIR 0.8<1.2; (III) SIR 1.2<2.0 and SIR>2.0 identified the municipalities with higher risk for dengue outbreaks. “Hot spots” are shown in the thematic maps. Significant correlations between SIR and two climate variables, two demographic variables and one socioeconomical variable were found. No significant correlations were found in the spatio-temporal model. The incidence of dengue exhibited an inconstant and unpredictable variation every year. The highest rates of dengue are concentrated in geographical clusters with lower surface pressure, rainfall and altitude, but also in municipalities with higher degree of urbanization and better socioeconomic conditions. Nevertheless, annual consolidated variations in climatic features do not influence in the epidemic yearly pattern of dengue in southeastern Brazil.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant of concern has spread rapidly across Brazil since late 2020, causing substantial infection and death waves. Here we used individual-level patient records after hospitalization with suspected or confirmed coronavirus disease 2019 (COVID-19) between 20 January 2020 and 26 July 2021 to document temporary, sweeping shocks in hospital fatality rates that followed the spread of Gamma across 14 state capitals, during which typically more than half of hospitalized patients aged 70 years and older died. We show that such extensive shocks in COVID-19 in-hospital fatality rates also existed before the detection of Gamma. Using a Bayesian fatality rate model, we found that the geographic and temporal fluctuations in Brazil’s COVID-19 in-hospital fatality rates were primarily associated with geographic inequities and shortages in healthcare capacity. We estimate that approximately half of the COVID-19 deaths in hospitals in the 14 cities could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization and pandemic preparedness are critical to minimize population-wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries. Analysis of individual-level patient records from Brazil reveals that the extensive shocks in COVID-19 mortality rates are associated with pre-pandemic geographic inequities as well as shortages in healthcare capacity during the pandemic.