São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

The vaccine roll-out has currently established a new trend in the fight against COVID-19. In many countries, as vaccination cover rises, the economic and social disruptions are being progressively reduced, bringing more confidence and hope to the population. However, a crucial debate is related to fair access to vaccines, which would lead to stepping up the pace of vaccination in developing countries. Another important issue is how immunization has influenced the control of the infection, deaths, and transmissibility of the new coronavirus in these countries. In this work we investigate the effects of the rate of vaccination on the COVID-19 epidemic curves, by employing a new data-driven methodology, formulated on the basis of a modified Susceptible-Infected-Recovered model and Machine Learning designs. This data-driven methodology is applied to assess the influence of the vaccines administered in Brazil, on the fight against the virus. The impacts of vaccine efficacy and immunization speed are also explored in our study. Finally, we have found that the use of anti-SARS-CoV-2 vaccines with a low/moderate efficacy can be offset by immunizing a larger proportion of the population more quickly.

Building a velocity model is essential in seismic exploration and is used at all stages, including acquisition, processing and interpretation of seismic data. Reconstructing a subsurface image from seismic wavefields recorded at the surface (seismograms) requires accurate knowledge of the propagation velocities between the recording location and the image location at depth. Estimation of velocity models can also be used as initial models to recursively generate high-resolution velocity models through optimization algorithms. Machine learning is a field of artificial intelligence that uses computational techniques to give systems the ability to learn from a large volume of data. In particular, neural networks have been developed to reconstruct subsurface parameters, i.e., the acoustic (compressional) wave velocity model, directly from raw seismic data. Using this principle as a starting point we will use two neural network approaches to solve the problem, where a GAN neural network and a ReGENN network will be used.

Resumo. O presente trabalho apresenta uma análise da evolução da Covid-19 no município de Araraquara, localizado no interior do Estado de São Paulo. Essa análise tem como objetivo investigar a eficiência do lockdown como estratégia de contenção no avanço do novo coronavírus, através da análise dos dados de casos confirmados, internações, óbitos e do número efetivo de reprodução do vírus. Para isto, foi realizado um estudo comparativo envolvendo os dados do município de São Carlos-SP, bem como o número de novos óbitos por 100 mil habitantes dos municípios paulistas de Presidente Prudente, Dracena e Jaú que, assim como São Carlos, não implementaram um regime de lockdown permanente. Os resultados obtidos demonstram que a adoção de medidas mais rígidas de redução de circulação de pessoas foi capaz de diminuir significativamente os indicadores relativos à disseminação da Covid-19 em Araraquara. A partir da análise dos dados, foi também possível constatar que, no mesmo período em que os índices da Covid-19 de Araraquara estavam em queda, o oposto ocorreu em São Carlos, que apresentou alta nos índices. Além disso, em relação à curva de novos óbitos ponderados por população, houve redução em Araraquara após o término do lockdown, e crescimento nos demais municípios analisados.