RT Journal Article
T1 COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning.
A1 Torres-Signes, Antoni
A1 Frías, María P.
A1 Ruiz-Medina, María D.
K1 COVID-19 - Mortalidad
K1 Análisis de datos
AB A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.
YR 2021
FD 2021-03-19
LK https://hdl.handle.net/10630/30289
UL https://hdl.handle.net/10630/30289
LA eng
NO Torres–Signes, A., Frías, M.P. & Ruiz-Medina, M.D. COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning. Stoch Environ Res Risk Assess 35, 2659–2678 (2021).
NO This work has been supported in part by projects PGC2018−099549-B-I00 of the Ministerio de Ciencia, Innovación y Universidades, Spain (co-funded with FEDER funds), and by grant A-FQM-345-UGR18 cofinanced by ERDF Operational Programme 2014-2020, and the Economy and Knowledge Council of the Regional Government of Andalusia, Spain. (In section Acknowledgements below, all sources of funding are also declared).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026