An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.

ABSTRACT:

Introduction

Machine learning models have been employed to predict COVID-19 infections and mortality, but many models were built on training and testing sets from different periods. The purpose of this study is to investigate the impact of temporality, i.e., the temporal gap between training and testing sets, on model performances for predicting COVID-19 infections and mortality. Furthermore, this study seeks to understand the causes of the impact of temporality.

Methods

This study used a COVID-19 surveillance dataset collected from Brazil in year 2020, 2021 and 2022, and built prediction models for COVID-19 infections and mortality using random forest and logistic regression, with 20 model features. Models were trained and tested based on data from different years and the s

SUBMITTER: Chen M 

PROVIDER: S-EPMC12020040 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature