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Predicting COVID-19 exposure risk perception using machine learning.

Nan Zou Bakkeli1

  • 1Centre for Research on Pandemics & Society; Consumption Research Norway, Oslo Metropolitan University, P.O. Box 4, St Olavs Plass, Oslo, 0130, Norway. Nan.Bakkeli@OsloMet.no.

BMC Public Health
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Summary
This summary is machine-generated.

Understanding COVID-19 risk perception is key for compliance and mental health. Key predictors include intervention compliance, work-life conflict, age, and gender, varying by pandemic phase.

Keywords:
COVID-19Exposure risksHealth inequalityInterpretable machine learningOccupational healthRisk perceptionSocial determinants of health

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Area of Science:

  • Public Health
  • Epidemiology
  • Behavioral Science

Background:

  • Self-perceived risk of exposure significantly influences adherence to COVID-19 preventive measures and mental health.
  • Identifying key predictors of perceived risk is crucial for effective public health interventions.
  • Understanding risk perception dynamics across different pandemic phases and social groups is essential.

Purpose of the Study:

  • To forecast and understand the predictors of perceived COVID-19 exposure risk.
  • To identify key factors influencing risk perception in the general population.
  • To inform targeted interventions for diverse social groups during the pandemic.

Main Methods:

  • Utilized survey data from 5001 Norwegians collected in 2020 and 2021.
  • Employed interpretable machine learning algorithms, including Gradient Boosting Machines, to predict perceived exposure risks.
  • Applied Shapley additive values to analyze feature importance and individual heterogeneity.

Main Results:

  • Gradient Boosting Machine models demonstrated strong performance in predicting perceived risk.
  • Top predictors included compliance with interventions, work-life conflict, age, and gender.
  • Predictor importance shifted from work/occupation in 2020 to living/behavioral factors in 2021, with significant individual variations.

Conclusions:

  • Findings aid in forecasting risk groups and early detection of vulnerable populations during health crises.
  • Results support the development of timely, tailored interventions for different social demographics.
  • Future public health policies must adapt to the diverse life situations influencing risk perception.