Brazil's continental dimension poses a challenge to the control of the spread of COVID-19. Due to the country specific scenario of high social and demographic heterogeneity, combined with limited testing capacity, lack of reliable data, under-reporting of cases, and restricted testing policy, the focus of this study is twofold: (i) to develop a generalized SEIRD model that implicitly takes into account the quarantine measures, and (ii) to estimate the response of the COVID-19 spread dynamics to perturbations/uncertainties. By investigating the projections of cumulative numbers of confirmed and death cases, as well as the effective reproduction number, we show that the model parameter related to social distancing measures is one of the most influential along all stages of the disease spread and the most influential after the infection peak. Due to such importance in the outcomes, different relaxation strategies of social distancing measures are investigated in order to determine which strategies are viable and less hazardous to the population. The results highlight the need of keeping social distancing policies to control the disease spread. Specifically, the considered scenario of abrupt social distancing relaxation implemented after the occurrence of the peak of positively diagnosed cases can prolong the epidemic, with a significant increase of the projected numbers of confirmed and death cases. An even worse scenario could occur if the quarantine relaxation policy is implemented before evidence of the epidemiological control, indicating the importance of the proper choice of when to start relaxing social distancing measures.

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Nesta nota técnica discutimos algumas medidas de relaxamento do distanciamento social e seus impactos no sentido epidemiológico com o objetivo de avaliar os efeitos nas projeções da epidemia da COVID-19 no Brasil e, em particular, no estado do Rio de Janeiro. A análise de possíveis cenários de relaxamento do distanciamento social é tema de grande relevância para auxiliar a estimar o momento mais apropriado para o retorno à normalidade do cotidiano. Neste contexto, discutimos a importância -- e as possíveis consequências -- de realizar o relaxamento do distanciamento social em um momento adequado. Os resultados indicam que a adoção de medidas de relaxamento gradual do distanciamento social, quando em situação de controle epidemiológico, são viáveis. Por outro lado, na ausência de verificação de controle epidemiológico, tanto medidas de relaxamento gradual quanto abruptas geram substancial aumento no número de casos confirmados e óbitos, além de evidências de considerável aumento no tempo necessário para a erradicação da doença. Portanto, no cenário em que não é possível aferir o controle epidemiológico, as medidas de relaxamento do distanciamento social estudadas nesta pesquisa não são recomendadas.

This work investigates the performance of the on-demand machine learning (ODML) algorithm introduced in Leal et al. (Transp. Porous Media133(2), 161–204, 2020) when applied to different reactive transport problems in heterogeneous porous media. This approach was devised to accelerate the computationally expensive geochemical reaction calculations in reactive transport simulations. We demonstrate that even with a strong heterogeneity present, the ODML algorithm speeds up these calculations by one to three orders of magnitude. Such acceleration, in turn, significantly advances the entire reactive transport simulation. The performed numerical experiments are enabled by the novel coupling of two open-source software packages: Reaktoro (Leal 2015) and Firedrake (Rathgeber et al. ACM Trans. Math. Softw.43(3), 2016). The first library provides the most recent version of the ODML approach for the chemical equilibrium calculations, whereas, the second framework includes the newly implemented conservative Discontinuous Galerkin finite element scheme for the Darcy problem, i.e., the Stabilized Dual Hybrid Mixed(SDHM) method Núñez et al. (Int. J. Model. Simul. Petroleum Industry, 6, 2012).