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Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach.

Peyman Namdar¹, Sajad Shafiekhani, Fatemeh Teymori

¹Author Affiliations: School of Medicine (Drs Namdar and Abdollahzade), Qazvin University of Medical Sciences (Ms Teymori); Department of Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (Dr Shafiekhani); and Student Research Center, School of Public Health (Mrs Maleki), and Social Determinants of Health Research Center (Dr Rafiei), Qazvin University of Medical Sciences, Iran.

Computers, Informatics, Nursing : CIN

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Summary

A forecasting model identified factors influencing COVID-19 infection risk in nurses. Access to personal protective equipment and training reduced risk, while exposure and stress increased it.

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Area of Science:

Epidemiology
Public Health
Health Informatics

Background:

Frontline health workers, particularly nurses, face significant COVID-19 infection risks.
Understanding risk factors is crucial for protecting healthcare personnel and maintaining healthcare system capacity.

Purpose of the Study:

To develop a predictive model for identifying nurses at high risk of COVID-19 infection.
To assess the impact of various factors on infection risk among nurses.

Main Methods:

Multivariate regression analysis and classification algorithms were employed.
Data from 220 nurses were analyzed, considering exposure, personal protective equipment (PPE) access and use, hand hygiene, stress, and training.
An artificial neural network (ANN) was developed for classification.

Main Results:

Access to PPE and training were associated with lower infection scores.
Exposure to COVID-19 cases and stress were linked to higher infection probability.
The ANN achieved 75.8% validation and 76.6% overall accuracy in classifying infected nurses.

Conclusions:

Predictive modeling can identify nurses at higher risk of COVID-19 infection.
Interventions focusing on PPE access, training, stress reduction, and exposure control are vital.
ANNs offer a valuable tool for healthcare workforce risk management during pandemics.