Persistências e rupturas no estudo do Coronavírus após a epidemia da COVID-19

O presente artigo, partindo do conjunto de palavras-chave de mais de 500 artigos relacionados à família do coronavírus, com publicações ao longo de 20 anos (2002 – 2022) e a formação de redes semânticas, analisa tais redes em sua topologia e características, assim como o conjunto de análises sobre as centralidades de nós, com o objetivo de vislumbrar uma métrica que apontasse temas e autores que colaboraram no estudo dos coronavírus antes da pandemia, aqueles que vieram antes da pandemia, mas tiveram contribuição relevante ao longo da pandemia nos anos estudados e aqueles que se somaram nos pós start da pandemia. No geral, busca-se analisar as persistências e rupturas da comunidade acadêmica frente ao novo Sars-Cov-2. Tal método se justifica como uma rota rápida e eficaz para filtrar bases teóricas e experimentais, pessoas e conhecimentos frente a um novo desafio, tal qual uma pandemia. Em linhas gerais, admite-se, neste trabalho, que palavras-chave são descritores importantes dos artigos publicados e, assim sendo, também cumprem um papel relevante como indicadores da agenda de pesquisa da comunidade de ciência. Percebe-se que as redes semânticas formadas por palavras-chave dos artigos selecionados mostraram aqui uma topologia com fortes características de uma rede mundo pequeno, o que evidencia uma forte ligação entre uma palavra-chave qualquer e sua vizinhança.Alternate :This article, based on the set of keywords of more than 500 articles related to the coronavirus family, with publications over 20 years (2002 - 2022) and the formation of semantic networks, analyzes such networks in their topology and characteristics, as well as the set of analyzes on the centralities of us, with the aim of envisioning a metric that would point out themes and authors who collaborated in the study of coronaviruses before the pandemic, those that came before the pandemic, but had a relevant contribution throughout the pandemic in the years studied and those that added up in the post-pandemic start. In general, we seek to analyze the persistence and ruptures of the academic community in the face of the new Sars-Cov-2. Such a method is justified as a quick and effective route to filter theoretical and experimental bases, people and knowledge in the face of a new challenge, such as a pandemic. In general terms, it is accepted, in this work, that keywords are important descriptors of published articles and, therefore, also play a relevant role as indicators of the research agenda of the science community. It is noticed that the semantic networks formed by keywords of the selected articles showed here a topology with strong characteristics of a small world network, which shows a strong connection between any keyword and its neighborhood.

Retrospective Cohort Study of COVID-19 in Patients of the Brazilian Public Health System with SARS-CoV-2 Omicron Variant Infection.

Several vaccines against COVID-19 are now available, based on different techniques and made by different laboratories spread around the world. With the roll out of the vaccination process in an advanced stage in many countries, the reduced risk of hospitalization due to the Omicron variant relative to the Delta variant infection, despite the higher transmission risk of Omicron, may lead to a misinterpretation of the results, as infection by Omicron is associated with a significant reduction in severe outcomes and shorter hospitalization time than the Delta variant. We compared the in-hospital mortality due to the Omicron (Jan-Mar 2022) with Gamma (Jan 2021) and Delta (Oct-Dec 2021) variants of patients in the Brazilian public health system. This study also discusses the decrease in booster vaccine effectiveness in patients hospitalized due to the Omicron variant compared with the Delta variant. Without a remodeling of vaccines for new variants, booster doses may be necessary with a shorter time interval.

Induction of a Consumption Pattern for Ethanol and Gasoline in Brazil

Historically, carbon dioxide emissions from transport have been a globally discussed and analyzed problem. The adoption of flex fuel vehicles designed to run ethanol–gasoline blends is important to mitigate these emissions. The main purpose of this paper is to analyze the impact of the ethanol–gasoline price ratio on different vehicle models, and discuss the opportunities to increase ethanol consumption from this perspective. Our analysis shows that the use of a unique fuel economy ratio for all flex–fuel vehicles in the country significantly reduces the opportunity of some customers to purchase hydrous ethanol. The paper also discusses possible actions to provide adequate information that may increase the possibility of fuelling vehicles with a high-level ethanol blend.

Synchronized spread of COVID-19 in the cities of Bahia, Brazil.

The COVID-19 pandemic, caused by the highly transmissible SARS-CoV-2 virus, has overloaded health systems in many contexts Conant and Wolfe (2008). Brazil has experienced more than 345,000 deaths, as of April/2021 Conant and Wolfe (2008), with dire consequences for the country's public and private health systems. This paper aims to estimate the synchronization graph between the cities' contagion waves from public COVID-19 data records. For this purpose, the Motif-Synchronization method Magwire et al. (2011) was applied to publicly available COVID-19 data records to determine the sequential relationship of occurrence of the waves among Bahia's cities. We find synchronization between waves of infection between cities, suggesting diffusion of the disease in Bahia and a potential role for inter-city transportation Saba et al. (2018), Saba et al. (2014), Araújo et al. (2018) in the dynamics of this phenomenon McKee and Stuckler (2020), Chinazzi et al. (2020), Tizzoni et al. (2014). Our main contribution lies in the use of the Motif-Synchronization method applied to COVID-19 data records, with the results revealing a pattern of disease spread that extends beyond city boundaries.

A Critical Analysis of the COVID-19 Hospitalization Network in Countries with Limited Resources.

To effectively combat the COVID-19 pandemic, countries with limited resources could only allocate intensive and non-intensive care units to a low number of regions. In this work, we evaluated the actual displacement of infected patients in search of care, aiming to understand how the networks of planned and actual hospitalizations take place. To assess the flow of hospitalizations outside the place of residence, we used the concepts of complex networks. Our findings indicate that the current distribution of health facilities in Bahia, Brazil, is not sufficient to effectively reduce the distances traveled by patients with COVID-19 who require hospitalization. We believe that unnecessary trips to distant hospitals can put both the sick and the healthy involved in the transport process at risk, further delaying the stabilization of the COVID-19 pandemic in each region of the state of Bahia. From the results found, we concluded that, to mitigate this situation, the implementation of health units in countries with limited resources should be based on scientific methods, and international collaborations should be established.

Correction: Correlation between hospitalized patients' demographics, symptoms, comorbidities, and COVID-19 pandemic in Bahia, Brazil.

[This corrects the article DOI: 10.1371/journal.pone.0243966.].

Correlation between hospitalized patients' demographics, symptoms, comorbidities, and COVID-19 pandemic in Bahia, Brazil.

In this paper, we provide a retrospective cohort study with patients that have been hospitalized for general or intensive care unit admission due to COVID-19, between March 3 and July 29, 2020, in the state of Bahia, Brazil. We aim to correlate those patients' demographics, symptoms and comorbidities, with the risk of mortality from COVID-19, length of hospital stay, and time from diagnosis to definitive outcome. On the basis of a dataset provided by the Health Secretary of the State of Bahia, we selected 3,896 hospitalized patients from a total of 154,868 COVID-19 patients that included non-hospitalized patients and patients with invalid registration in the dataset. Then, we statistically analyzed whether there was a significant correlation between the patient record data and the COVID-19 pandemic, and our main findings reinforced by the use of a multivariable logistic regression were that older age (Odds Ratio [OR] = 1.03, 95% Confidence Interval [CI] = 1.03-1.04, p-value (p) <0.001), an initial symptom of shortness of breath (OR = 1.88, 95% CI = 1.60-2.20, p < 0.001), and the presence of comorbidities, mainly chronic kidney disease (OR = 2.41, 95% CI = 1.67-3.48, p < 0.001) are related to an increased risk of mortality from COVID-19. On the other hand, sore throat (OR = 0.74, 95% CI = 0.58-0.95, p = 0.02) and length of hospital stay (OR = 0.96, 95% CI = 0.58-0.95, p < 0.001) are more related to a reduced risk of mortality from COVID-19. Moreover, a multivariable linear regression conducted with statistically significant variables (p < 0.05) showed that age (OR = 0.97, 95% CI = 0.95-0.98, p < 0.001) and time from diagnosis to definitive outcome (OR = 1.67, 95% CI = 1.64-1.71, p < 0.001) are associated with the length of hospital stay.

Scaling effect in COVID-19 spreading: The role of heterogeneity in a hybrid ODE-network model with restrictions on the inter-cities flow.

The new Covid-19 pandemic has left traces of suffering and devastation to individuals of almost all countries worldwide and severe impact on the global economy. Understanding the clinical characteristics, interactions with the environment, and the variables that favor or hinder its dissemination help the public authorities in the fight and prevention, leading for a rapid response in society. Using models to estimate contamination scenarios in real time plays an important role. Population compartments models based on ordinary differential equations (ODE) for a given region assume two homogeneous premises, the contact mechanisms and diffusion rates, disregarding heterogeneous factors as different contact rates for each municipality and the flow of contaminated people among them. This work considers a hybrid model for covid-19, based on local SIR models and the population flow network among municipalities, responsible for a complex lag dynamic in their contagion curves. Based on actual infection data, local contact rates ( ß ) are evaluated. The epidemic evolution at each municipality depends on the local SIR parameters and on the inter-municipality transport flow. When heterogeneity of ß values and flow network are included, forecasts differ from those of the homogeneous ODE model. This effect is more relevant when more municipalities are considered, hinting that the latter overestimates new cases. In addition, mitigation scenarios are assessed to evaluate the effect of earlier interventions reducing the inter-municipality flux. Restricting the flow between municipalities in the initial stage of the epidemic is fundamental for flattening the contamination curve, highlighting advantages of a contamination lag between the capital curve and those of other municipalities in the territories.