Predicting COVID-19 exposure risk perception using machine learning.

ABSTRACT:

Background

Self-perceived exposure risk determines the likelihood of COVID-19 preventive measure compliance to a large extent and is among the most important predictors of mental health problems. Therefore, there is a need to systematically identify important predictors of such risks. This study aims to provide insight into forecasting and understanding risk perceptions and help to adjust interventions that target various social groups in different pandemic phases.

Methods

This study was based on survey data collected from 5001 Norwegians in 2020 and 2021. Interpretable machine learning algorithms were used to predict perceived exposure risks. To detect the most important predictors, the models with best performance were chosen based on predictive errors and explained varian

SUBMITTER: Bakkeli NZ 

PROVIDER: S-EPMC10353112 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature