Universidade de Pernambuco

About: Universidade de Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Medicine. The organization has 6147 authors who have published 6948 publications receiving 73648 citations.

Heg.IA: An intelligent system to support diagnosis of Covid-19 based on blood tests

Abstract: A new kind of coronavirus, the SARS-Cov2, started the biggest pandemic of the century. It has already killed more than 250,000 people. Because of this, it is necessary quick and precise diagnosis test. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results takes too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact in hematological parameters. This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. We tested several machine learning methods, and we achieved high classification performance: 95.159% ± 0.693 of overall accuracy, kappa index of 0.903 ± 0.014, sensitivity of 0.968 ± 0.007, precision of 0.938 ± 0.010 and specificity of 0.936 ± 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use.

Using Accelerated Life Tests to Estimate Time to Software Aging Failure

Abstract: Software aging is a phenomenon defined as the continuing degradation of software systems during runtime, being particularly noticeable in long-running applications Aging-related failures are very difficult to observe, because the accumulation of aging effects usually requires a long-term execution Thus, collecting a statistically significant sample of times to aging-related failures so as to estimate the system’s lifetime distribution is a very hard task This is an important problem that prevents many experimental and analytical studies, mainly those focused on modeling of software aging aspects, of using representative parameter values In this paper we propose and evaluate the use of quantitative accelerated life tests (QALT) to reduce the time to obtain the lifetime distribution of systems that fail due to software aging Since QALT was developed for hardware failures, in this paper, we adapt it to software aging experiments We test the proposed approach experimentally, estimating the lifetime distribution of a real web server system The accuracy of the estimated distribution is evaluated by comparing its reliability estimates with a sample of failure times observed from the real system under test The mean time to failure calculated from the real sample falls inside the 90% confidence interval constructed from the estimated lifetime distribution, demonstrating the high accuracy of the estimated model The proposed approach reduces the time required to obtain the failure times by a factor of seven, for the real system investigated

Modelling the Force of Infection for Hepatitis A in an Urban Population-Based Survey: A Comparison of Transmission Patterns in Brazilian Macro-Regions

Abstract: Background: This study aimed to identify the transmission pattern of hepatitis A (HA) infection based on a primary dataset from the Brazilian National Hepatitis Survey in a pre-vaccination context. The national survey conducted in urban areas disclosed two epidemiological scenarios with low and intermediate HA endemicity. Methods: A catalytic model of HA transmission was built based on a national seroprevalence survey (2005 to 2009). The seroprevalence data from 7,062 individuals aged 5–69 years from all the Brazilian macro-regions were included. We built up three models: fully homogeneous mixing model, with constant contact pattern; the highly assortative model and the highly assortative model with the additional component accounting for contacts with infected food/water. Curves of prevalence, force of infection (FOI) and the number of new infections with 99% confidence intervals (CIs) were compared between the intermediate (North, Northeast, Midwest and Federal District) and low (South and Southeast) endemicity areas. A contour plot was also constructed. Results: The anti- HAV IgG seroprevalence was 68.8% (95% CI, 64.8%–72.5%) and 33.7% (95% CI, 32.4%–35.1%) for the intermediate and low endemicity areas, respectively, according to the field data analysis. The models showed that a higher force of infection was identified in the 10- to 19-year-old age cohort (,9,000 infected individuals per year per 100,000 susceptible persons) in the intermediate endemicity area, whereas a higher force of infection occurred in the 15- to 29-yearold age cohort (,6,000 infected individuals per year per 100,000 susceptible persons) for the other macro-regions. Conclusion: Our findings support the shift of Brazil toward intermediate and low endemicity levels with the shift of the risk of infection to older age groups. These estimates of HA force of infection stratified by age and endemicity levels are useful information to characterize the pre-vaccination scenario in Brazil.