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

TL;DR

This paper introduces a novel multivariate curve regression and Bayesian analysis approach for COVID-19 mortality forecasting, comparing it with machine learning models using empirical data from Spain's first wave.

Contribution

It presents a new combined framework of cyclical curve log-regression and spatial residual correlation analysis for COVID-19 mortality prediction, integrating soft-data Bayesian methods.

Findings

The proposed approach effectively models COVID-19 mortality trends.

Machine learning models show varying performance in hard- and soft-data contexts.

The methodology can be adapted to other regions and subsequent COVID-19 waves.

Abstract

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.