A time-course prediction model of global COVID-19 mortality.

ABSTRACT:

Introduction

The COVID-19 pandemic has caused over 6 million deaths worldwide and is a significant cause of mortality. Mortality dynamics vary significantly by country due to pathogen, host, social and environmental factors, in addition to vaccination and treatments. However, there is limited data on the relative contribution of different explanatory variables, which may explain changes in mortality over time. We, therefore, created a predictive model using orthogonal machine learning techniques to attempt to quantify the contribution of static and dynamic variables over time.

Methods

A model was created using Partial Least Squares Regression trained on data from 2020 to rank order the significance and effect size of static variables on mortality per country. This model enab

SUBMITTER: Ciaccio M 

PROVIDER: S-EPMC10773778 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature