COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning
This work addresses mortality prediction for public health planning during pandemics, but appears incremental as it applies existing statistical and ML methods to COVID-19 data.
The authors tackled COVID-19 mortality forecasting in Spanish Communities during the first wave by developing a multiple objective space-time approach combining cyclical curve log-regression and multivariate time series analysis, and compared it with machine learning regression models using cross-validation and bootstrapping.
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.