COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning

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 subse...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2021-12, Vol.35 (12), p.2659-2678
Main Authors: Torres–Signes, Antoni, Frías, María P., Ruiz-Medina, María D.
Format: Article
Language:English
Subjects:
Aquatic Pollution
Bayesian analysis
Chemistry and Earth Sciences
Comparative studies
Computational Intelligence
Computer Science
Confidence intervals
Coronaviruses
Correlation analysis
COVID-19
Earth and Environmental Science
Earth Sciences
Empirical analysis
Environment
Learning algorithms
Machine learning
Math. Appl. in Environmental Science
Mathematical models
Mortality
Multivariate analysis
Original Paper
Physics
Probability Theory and Stochastic Processes
Regression analysis
Regression models
Spatial analysis
Statistical analysis
Statistics for Engineering
Time series
Waste Water Technology
Water Management
Water Pollution Control
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Summary: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.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-021-02021-0