Introduction: Five months after the first confirmed case of COVID-19 in Brazil, the country has the second highest number of cases in the world. Without any scientifically proven drug or vaccine available combined with COVID-19's high transmissivity, slowing down the spread of the infection is a challenge. In an attempt to save the economy, the Brazilian government is slowly beginning to allow nonessential services to reopen for in-person customers. Methods: In this study, we analyze, based on data analysis and statistics, how other countries evolve and under which conditions they decided to resume normal activity. In addition, due to the heterogeneity of Brazil, we explore Brazilian data of COVID-19 from the State Health Secretaries to evaluate the situation of the pandemic within the states. Results: Results show that while other countries have flattened their curves and present low numbers of active cases, Brazil continues to see an increase in COVID-19 patients. Furthermore, a number of important states are easing restrictions despite a high percentage of confirmed cases. Conclusions: All analyses show that Brazil is not ready for reopening, and the premature easing of restrictions may increase the number of COVID-19-related deaths and cause the collapse of the public health system.

BackgroundThe organization of the canonical code has intrigued researches since it was first described. If we consider all codes mapping the 64 codes into 20 amino acids and one stop codon, there are more than 1.51×1084 possible genetic codes. The main question related to the organization of the genetic code is why exactly the canonical code was selected among this huge number of possible genetic codes. Many researchers argue that the organization of the canonical code is a product of natural selection and that the code’s robustness against mutations would support this hypothesis. In order to investigate the natural selection hypothesis, some researches employ optimization algorithms to identify regions of the genetic code space where best codes, according to a given evaluation function, can be found (engineering approach). The optimization process uses only one objective to evaluate the codes, generally based on the robustness for an amino acid property. Only one objective is also employed in the statistical approach for the comparison of the canonical code with random codes. We propose a multiobjective approach where two or more objectives are considered simultaneously to evaluate the genetic codes.ResultsIn order to test our hypothesis that the multiobjective approach is useful for the analysis of the genetic code adaptability, we implemented a multiobjective optimization algorithm where two objectives are simultaneously optimized. Using as objectives the robustness against mutation with the amino acids properties polar requirement (objective 1) and robustness with respect to hydropathy index or molecular volume (objective 2), we found solutions closer to the canonical genetic code in terms of robustness, when compared with the results using only one objective reported by other authors.ConclusionsUsing more objectives, more optimal solutions are obtained and, as a consequence, more information can be used to investigate the adaptability of the genetic code. The multiobjective approach is also more natural, because more than one objective was adapted during the evolutionary process of the canonical genetic code. Our results suggest that the evaluation function employed to compare genetic codes should consider simultaneously more than one objective, in contrast to what has been done in the literature.

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