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.

Reliable data are essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of COVID-19, for example, such uncertainties are mainly motivated by large-scale underreporting of cases due to reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of models, we propose strategies capable of improving the ability to predict the spread of the diseases. Using a compartmental model in a COVID-19 study case, we show that the regularization of data by means of Gaussian process regression can reduce the variability of successive forecasts, improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.

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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 puts forward a dynamical population model to qualitatively reproduce the phenomena of apparent competition and apparent mutualism found in an experiment with two arthropods being attacked by a predator in a context of pest biological control in greenhouse crops. The two agricultural pests consist of one species of thrips (Frankliniella occidentalis (Pergande 1895)) and one species of whiteflies (Trialeurodes vaporariorum Westwood, 1956), and the shared predator is a predatory mite (Amblyseius swirskii Athias-Herriot, 1962). The predatory mite is the biocontrol agent employed in order to achieve the biological control. The proposed model successfully reproduces this density mediated indirect interactions between pests when their carrying capacities are increased. Moreover, the pests' final population levels may depend on their initial densities and those of their predator. With these results, the proposed model may have the potential to assess whether these indirect pest interactions disrupt or enhance biological control. Additionally, it can also be used as an ancillary tool to theoretically assess the effects of pest biocontrol strategies in the referred experimental setup.

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