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Research on predictions related to the spread of the novel coronavirus are crucial in decision-making to mitigate the disease. Computational simulations are often used as a basis for forecasting the dynamics of epidemics and, for this purpose, compartmental models have been widely used to assess the situation resulting from the spread of the disease in the population. Reliable data is essential to obtain adequate simulations. However, several political, economic, and social factors have caused inconsistencies in the reported data, which are reflected in the capacity for realistic simulations and predictions. Such uncertainties are mainly motivated by a large-scale underreporting of cases due to the reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of compartmental models, we propose strategies capable of improving the ability to predict the spread of the disease. We show that the regularization of data by means of Gaussian Process Regression can reduce the variability of successive forecasts, thus 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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?:doi
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?:doi
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10.1101/2020.12.17.20248389
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document_parses/pdf_json/0f66157fcfd5417f354243a9ec88e8d3f5ce3da0.json
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?:title
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Enhancing the estimation of compartmental model parameters for COVID-19 data with a high level of uncertainty
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