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As the main methods of the coronavirus disease (COVID-19) transmission are air and physical contact, actions to mitigate and suppress its spread must be developed in order to change population dynamics and provide efficient control strategies. Here, these actions are described as a simple heuristic framework to establish public policies. Two control systems were studied: the first organized in the form of an algorithm stratified into three levels and the second as a minimization problem similar to optimal control strategies, applied to both social distancing and vaccination. The possible effects of these actions are modeled and applied to an extension of the Susceptible - Infected - Removed (SIR) compartmental model. The control system is developed, which is organized in the form of an algorithm stratified into three levels. These levels intend to represent social distancing strategies implemented by sanitary authorities around the globe, representing stronger or weaker grades of isolation intensity according to the ability of the healthcare system to cope with symptomatic individuals. The algorithm control is applied in a simulation, and the results give evidence of the effectiveness of the procedures adopted against the coronavirus. The model dynamics are analyzed and validated with simulations considering parameters obtained from epidemiological data from Brazil and Uruguay and in a more detailed way for three Brazilian states: São Paulo, Minas Gerais and Rio de Janeiro. The model was validated using cumulative data on cases and deaths. For cases of death, the results were satisfactory, while for case data, the response was reasonable, considering the possibility of adding delays or variations in parameters in the model. In addition, the effective reproduction number was proposed for the cities studied in Brazil, the result being relevant because it has a qualitative behavior similar to that published by official centers. This paper also discusses the implementation and optimization of social distancing and vaccination control strategies, considering different parameters and their effects on reducing the number of cases and deaths. Model simulations present promising results for developing strategies to attack COVID-19 dissemination. [ABSTRACT FROM AUTHOR]

Source coding maps elements from an information source to a sequence of alphabetic symbols. Then, the source symbols can be recovered exactly from the binary units. In this paper, we derive an approach that includes information variation in the source coding. The approach is more realistic than its standard version. We employ the Shannon entropy for coding the sequences of a source. Our approach is also helpful for short sequences when the central limit theorem does not apply. We rely on a quantifier of the information variation as a source. This quantifier corresponds to the second central moment of a random variable that measures the information content of a source symbol; that is, considering the standard deviation. An interpretation of typical sequences is also provided through this approach. We show how to use a binary memoryless source as an example. In addition, Monte Carlo simulation studies are conducted to evaluate the performance of our approach. We apply this approach to two real datasets related to purity and wheat prices in Brazil. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak caused several negative effects in industries of all sizes and in all parts of the world, leading academic and practitioners to ask whether organizations could have been better prepared to face disruptive situations. This paper aims to analyze business continuity practices performed by Brazilian industries. A survey was conducted with academics who work in the field of organizational resilience and business continuity and are familiar with the reality of Brazilian companies in the industrial sector. The participants assessed 16 practices (P) proposed by the ISO 22301:2020, considering two categories: large industries (LI) and small and medium-sized industries (SMI). Data analysis was performed using Hierarchical Cluster Analysis, frequency analysis, Fuzzy TOPSIS and sensitivity analysis. For LIs, P4 (leaders conduct periodic critical analyses of practices) was considered the practice with the best application rate, while for SMIs, P2 (understand stakeholders' needs and expectations, and use information in business continuity management) was chosen. In all scenarios tested for LIs and SMIs, P8 (well-structured systematic processes to analyze the impact of abnormal situations on their business and the potential risks of a disruption) and P16 (periodic audits of their business continuity management activities to identify opportunities for improvement, and information record) are in the bottom quartile. When compared to LIs in the Brazilian context, SMIs exhibit more profound deficiencies in terms of applying business continuity practices. The findings of this study can be of great value to assist managers in improving organizational resilience. Organizations should be better prepared to face future disruptive events, whether biological, social, technological, or economic. [ABSTRACT FROM AUTHOR]