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Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models
[article]
2020
bioRxiv
pre-print
BACKGROUND - Since the first reports of COVID-19, decision-makers have been using traditional epidemiological models to predict the days to come. However, the enhancement of computational power, the demand for adaptable predictive frameworks, the short past of the disease, and uncertainties related to input data and prediction rules, also make other classical and machine learning techniques viable options. OBJECTIVE - This study investigates the efficiency of six models in forecasting COVID-19
doi:10.1101/2020.10.09.332908
fatcat:qt3ryqddzrc7vnypbbqfl5j6le