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.
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
F. Hoffmann-La Roche Ltd（罗氏制药公司）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；MIT Critical Data, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Harvard-MIT Health Sciences & Technology（哈佛-麻省理工健康科学与技术研究所医学工程与科学研究所计算生理学实验室MIT关键数据）；University Hospital of Tübingen（蒂宾根大学医院）；Kantonsspital Winterthur（温特图尔州立医院）
Parametric quantile regression models for fitting double bounded response with application to COVID-19 mortality rate data
在本文中，我们基于Power Johnson SB分布Cancho et al.（2020）开发了两个全参数分位数回归模型，用于对不同分位数的单位区间响应进行建模。特别地，条件分布由幂约翰逊SB分布建模。采用极大似然法估计模型参数。模拟研究进行评估的极大似然估计在有限样本的性能。此外，我们还讨论了残差和影响诊断工具。我们的建议的有效性以不同国家COVID-19死亡率的两个数据集为例。
Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
Huma Therapeutics Limited（Huma Therapeutics有限公司）；Imperial College London（帝国理工学院）；University of Belgrade（贝尔格莱德大学）；University of Exeter（埃克塞特大学）；Johns Hopkins Bloomberg School Public Health（约翰斯·霍普金斯大学彭博公共卫生学院）
Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation
MIT Sloan School of Management（麻省理工学院斯隆管理学院）；Operations Research Center（运筹学中心）；MIT Center for Statistics（麻省理工学院统计中心）；Dana Farber Cancer Institute（达纳·法伯癌症研究所）；Harvard School of Public Health（哈佛大学公共卫生学院）
