2026-06-07T08:01:53Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/302892026-02-03T10:52:42Zcom_10630_2254col_10630_37953

COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning. Torres-Signes, Antoni Frías, María P. Ruiz-Medina, María D. COVID-19 - Mortalidad Análisis de datos 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. 2024-02-09T11:53:59Z 2024-02-09T11:53:59Z 2024-02-09T11:53:59Z 2021-03-19 journal article 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). https://hdl.handle.net/10630/30289 10.1007/s00477-021-02021-0 eng open access