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Background:The COVID-19 situation in Brazil is complex due to large differences in the shape and size of regional epidemics. Understanding these patterns is crucial to understand future outbreaks of SARS-CoV-2 or other respiratory pathogens in the country.Methods:We tested 97,950 blood donation samples for IgG antibodies from March 2020 to March 2021 in 8 of Brazil’s most populous cities. Residential postal codes were used to obtain representative samples. Weekly age- and sex-specific seroprevalence were estimated by correcting the crude seroprevalence by test sensitivity, specificity, and antibody waning.Results:The inferred attack rate of SARS-CoV-2 in December 2020, before the Gamma variant of concern (VOC) was dominant, ranged from 19.3% (95% credible interval [CrI] 17.5–21.2%) in Curitiba to 75.0% (95% CrI 70.8–80.3%) in Manaus. Seroprevalence was consistently smaller in women and donors older than 55 years. The age-specific infection fatality rate (IFR) differed between cities and consistently increased with age. The infection hospitalisation rate increased significantly during the Gamma-dominated second wave in Manaus, suggesting increased morbidity of the Gamma VOC compared to previous variants circulating in Manaus. The higher disease penetrance associated with the health system’s collapse increased the overall IFR by a minimum factor of 2.91 (95% CrI 2.43–3.53).Conclusions:These results highlight the utility of blood donor serosurveillance to track epidemic maturity and demonstrate demographic and spatial heterogeneity in SARS-CoV-2 spread.Funding:This work was supported by Itaú Unibanco ‘Todos pela Saude’ program; FAPESP (grants 18/14389-0, 2019/21585-0); Wellcome Trust and Royal Society Sir Henry Dale Fellowship 204311/Z/16/Z; the Gates Foundation (INV- 034540 and INV-034652); REDS-IV-P (grant HHSN268201100007I); the UK Medical Research Council (MR/S0195/1, MR/V038109/1); CAPES; CNPq (304714/2018-6); Fundação Faculdade de Medicina; Programa Inova Fiocruz-CE/Funcap - Edital 01/2020 Number: FIO-0167-00065.01.00/20 SPU N°06531047/2020; JBS – Fazer o bem faz bem.

BackgroundThe 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 the Gamma variant during the second wave in Manaus and the protection conferred by previous infection, we analyzed a cohort of repeat blood donors to identify anti-SARS-CoV-2 antibody boosting as a means to infer reinfection.MethodsWe tested serial blood samples from unvaccinated repeat blood donors in Manaus for the presence of anti-SARS-CoV-2 IgG antibody. Donors were required to have three or more donations and at least one donation during each epidemic wave. Donors were tested with two assays that display waning in early convalescence, enabling the detection of reinfection-induced boosting. 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.ResultsFrom 3,655 repeat blood donors, 238 met all inclusion criteria, and 223 had enough residual sample volume to perform both serological assays. Using a strict serological definition of reinfection, 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.ConclusionsReinfection due to 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.

Attack rate in Manaus Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence peaked in Manaus, Brazil, in May 2020 with a devastating toll on the city's inhabitants, leaving its health services shattered and cemeteries overwhelmed. Buss et al. collected data from blood donors from Manaus and São Paulo, noted when transmission began to fall, and estimated the final attack rates in October 2020 (see the Perspective by Sridhar and Gurdasani). Heterogeneities in immune protection, population structure, poverty, modes of public transport, and uneven adoption of nonpharmaceutical interventions mean that despite a high attack rate, herd immunity may not have been achieved. This unfortunate city has become a sentinel for how natural population immunity could influence future transmission. Events in Manaus reveal what tragedy and harm to society can unfold if this virus is left to run its course. Science , this issue p. 288 ; see also p. 230

The recent development of Geographic Information Systems and the high availability of geolocation devices have been stimulating significant growth in geographic data. The correct usage of geographic information is severely dependent on data quality and integration capacity. To find a suitable way to integrate geographic data, it is essential to define a strategy to identify as many errors as possible in that data. In this context, this project exploits the standard ISO 19157 and proposes a taxonomy which extends its main elements, focusing on the geographic data problems. The development of the taxonomy of geographic errors started by a survey of past contributions covering other taxonomies, errors, and data quality. This phase organized a set of frequent problems in geographic data. After that, the problems were classified considering the structure of standard ISO 19157. Taxonomy of errors can help users identify and solve problems on data, improving their integration and sharing potential.

Objective We evaluated the association between obesity and ABO blood group antigens in a large sample of blood donors. Methods This cross-sectional study included 549,690 individuals who donated blood at three blood donation centers in Brazil between 2006 and 2012. Subjects were categorized by body mass index (BMI) as normal weight, overweight and obese. A multivariate analysis was performed by Ordinal Logistic Regression to evaluate associations between BMI and ABO blood group by gender and age. Results Among the participants 37% were overweight and 15.3% were obese. The majority of participants were male (65.4%), less than 44 years old (86.6%) and had greater than 11 years of education (61.4%). Blood group O had the highest prevalence (48.9%) and group AB had the lowest prevalence (3.7%) among participants. The mean BMI was slightly above 25 kg/m2, the lower limit of an overweight classification in all ABO blood groups. Associations between specific ABO antigens and BMI were identified that differed according to gender. The O and B blood groups were associated with a greater prevalence of obesity in women and a lower prevalence in men. Conclusions Carriage of specific alleles in the ABO blood group may differently influence BMI, according to gender.

The diagnosis of Chagas disease is based on the detection of Trypanosoma cruzi (T. cruzi)-specific antibodies. Nonetheless, there is concern about the sensitivity of current serological assays due to reports of T. cruzi PCR positivity among seronegative individuals. The aim of this study was to evaluate if T. cruzi seronegative infections occur in endemic areas. We recruited 2,157 individuals that were identified as having Chagas disease in a public health system database of an endemic region in Brazil. All participants were interviewed and 2,091 had a sample collected for serological and PCR testing. From these, 149 (7.1%) had negative serological results. PCR was positive in 610 samples (31.4%) of the 1,942 seropositive samples but in none of the 149 samples from seronegative participants. True T. cruzi seronegative infections seem to be rare (95% CI 0-3.7) and should not be a concern for blood supply, which relies on antibody screening.

Over time, data warehouse (DW) systems have become more difficult to develop because of the growing heterogeneity of data sources. Despite advances in research and technology, DW projects are still too slow for pragmatic results to be generated. Here, we address the following question: how can the complexity of DW development for integration of heterogeneous transactional information systems be reduced? To answer this, we proposed methodological guidelines based on cycles of conceptual modeling and data analysis, to drive construction of a modular DW system. These guidelines were applied to the blood donation domain, successfully reducing the complexity of DW development.

Data quality assessment outcomes are essential for analytical processes, especially for big data environment. Its efficiency and efficacy depends on automated solutions, which are determined by understanding the problem associated with each data defect. Despite the considerable number of works that describe data defects regarding to accuracy, completeness and consistency, there is a significant heterogeneity of terminology, nomenclature, description depth and number of examined defects. To cover this gap, this work reports a taxonomy that organizes data defects according to a three-step methodology. The proposed taxonomy enhances the descriptions and coverage of defects with regard to the related works, and also supports certain requirements of data quality assessment, including the design of semi-supervised solutions to data defect detection.

BACKGROUND: A major problem in Chagas disease donor screening is the high frequency of samples with inconclusive results. The objective of this study was to describe patterns of serologic results among donors to the three Brazilian REDS-II blood centers and correlate with epidemiologic characteristics. STUDY DESIGN AND METHODS: The centers screened donor samples with one Trypanosoma cruzi lysate enzyme immunoassay (EIA). EIA-reactive samples were tested with a second lysate EIA, a recombinant-antigen based EIA, and an immunfluorescence assay. Based on the serologic results, samples were classified as confirmed positive (CP), probable positive (PP), possible other parasitic infection (POPI), and false positive (FP). RESULTS: In 2007 to 2008, a total of 877 of 615,433 donations were discarded due to Chagas assay reactivity. The prevalences (95% confidence intervals [CIs]) among first-time donors for CP, PP, POPI, and FP patterns were 114 (99-129), 26 (19-34), 10 (5-14), and 96 (82-110) per 100,000 donations, respectively. CP and PP had similar patterns of prevalence when analyzed by age, sex, education, and location, suggesting that PP cases represent true T. cruzi infections; in contrast the demographics of donors with POPI were distinct and likely unrelated to Chagas disease. No CP cases were detected among 218,514 repeat donors followed for a total of 718,187 person-years. CONCLUSION: We have proposed a classification algorithm that may have practical importance for donor counseling and epidemiologic analyses of T. cruzi–seroreactive donors. The absence of incident T. cruzi infections is reassuring with respect to risk of window phase infections within Brazil and travel-related infections in nonendemic countries such as the United States.

BackgroundNucleoside/nucleotide analogue (NA) treatment causes selection pressure for HBV strains carrying mutations conferring NA resistance. Drug-resistance mutations occur in the reverse transcriptase (RT) region of the HBV polymerase gene and spontaneously arise during viral replication. These mutations can also alter the hepatitis B surface (HBs) protein and in some cases reduce binding to HBs antibodies. The spread of NA-resistant HBV may impact the efficacy of antiviral treatment and hepatitis B immunization programmes. In this study, we used direct sequencing to assess the occurrence of HBV carrying known mutations that confer NA resistance in the largest cohort of treatment-naive patients with chronic hepatitis B (CHB) to date.MethodsHBV DNA samples isolated from 702 patients were sequenced and the RT region subjected to mutational analysis.ResultsThere was high genetic variability among the HBV samples analysed: A1 (63.7%), D3 (14.5%), A2 (3.3%), A3 (0.1%), B1 (0.1%), B2 (0.1%), C2 (0.9%), D1 (0.9%), D2 (4.6%), D4 (5.1%), D unclassified sub-genotype (0.7%), E (0.6%), F2a (4.6%), F4 (0.4%) and G (0.4%). HBV strains harbouring mutations conferring NA resistance alone or combined with compensatory mutations were identified in 1.6% (11/702) of the patients.ConclusionsHBV strains harbouring resistance mutations can comprise the major population of HBV quasispecies in treatment-naive patients. In Brazil, there is a very low frequency of untreated patients who are infected with these strains. These findings suggest that the spread and natural selection of drug-resistant HBV is an uncommon event and/or most of these strains remain unstable in the absence of NA selective pressure.

Despite the recent advances in the area of Business Process Management (BPM), today’s business processes have largely been implemented without clearly defined conceptual modeling. This results in growing difficulties for identification, maintenance, and reuse of rules, processes, and control-flow patterns. To mitigate these problems in future implementations, we propose a new approach to business process modeling using conceptual schemas, which represent hierarchies of concepts for rules and processes shared among collaborating information systems. This methodology bridges the gap between conceptual model description and identification of actual control-flow patterns for workflow implementation. We identify modeling guidelines that are characterized by clear phase separation, step-by-step execution, and process building through diagrams and tables. The separation of business process modeling in seven mutually exclusive phases clearly delimits information technology from business expertise. The sequential execution of these phases leads to the step-by-step creation of complex control-flow graphs. The process model is refined through intuitive table and diagram generation in each phase. Not only does the rigorous application of our modeling framework minimize the impact of rule and process changes, but it also facilitates the identification and maintenance of control-flow patterns in BPM-based information system architectures.

Recently, a lot of work has been done on formalization of business process specification, in particular, using Petri nets and process algebra. However, these efforts usually do not explicitly address complex business process development, which necessitates the specification, coordination, and synchronization of a large number of business steps. It is imperative that these atomic tasks are associated correctly and monitored for countless dependencies. Moreover, as these business processes grow, they become critically reliant on a large number of split and merge points, which additionally increases modeling complexity. Therefore, one of the central challenges in complex business process modeling is the composition of dependent business steps. We address this challenge and introduce a formally correct method for automated composition of algebraic expressions in complex business process modeling based on acyclic directed graph reductions. We show that our method generates an equivalent algebraic expression from an appropriate acyclic directed graph if the graph is well-formed and series-parallel. Additionally, we encapsulate the reductions in an algorithm that transforms business step dependencies described by users into digraphs, recognizes structural conflicts, identifies Wheatstone bridges, and finally generates algebraic expressions.

A large number of DNA sequencing projects all over the world have yielded a fantastic amount of data, whose analysis is, currently, a big challenge for computational biology. The limiting step in this task is the integration of large volumes of data stored in highly heterogeneous repositories of genomic and cDNA sequences, as well as gene expression results. Solving this problem requires automated analytical tools to optimize operations and efficiently generate knowledge. This paper presents an information flow model , called GenFlow, that can tackle this analytical task.