We discuss a statistical framework to monitor and predict the COVID-19 epidemic outbreak. More specifically, we use segmented regression to quantify the deceleration of epidemic spreading likely due to the effectiveness of lockdown policy and parametric nonlinear regression to predict the number of confirmed cases in the short-to-medium term.
Muggeo, V., Sottile, G., Genova, V.G., Bertolazzi, G., Consiglio, A., Porcu, M. (2022). Statistical Modelling to Monitor and to Predict the Epidemic Evolution. In COVID-19 and Communities: The University of Palermo's Voice and Analyses During the Pandemic (pp. 15-21). Springer [10.1007/978-3-030-88622-6_3].
Statistical Modelling to Monitor and to Predict the Epidemic Evolution
Muggeo, Vito
;Sottile, Gianluca;Genova, Vincenzo G.;Bertolazzi, Giorgio;Consiglio, Andrea;Porcu, Mariano
2022-03-05
Abstract
We discuss a statistical framework to monitor and predict the COVID-19 epidemic outbreak. More specifically, we use segmented regression to quantify the deceleration of epidemic spreading likely due to the effectiveness of lockdown policy and parametric nonlinear regression to predict the number of confirmed cases in the short-to-medium term.
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