تقرير
A Probabilistic Domain-knowledge Framework for Nosocomial Infection Risk Estimation of Communicable Viral Diseases in Healthcare Personnel: A Case Study for COVID-19
| العنوان: | A Probabilistic Domain-knowledge Framework for Nosocomial Infection Risk Estimation of Communicable Viral Diseases in Healthcare Personnel: A Case Study for COVID-19 |
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| المؤلفون: | Huynh, Phat K., Setty, Arveity R., Yadav, Om P., Le, Trung Q. |
| سنة النشر: | 2021 |
| المجموعة: | Statistics |
| مصطلحات موضوعية: | Statistics - Applications, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | Hospital-acquired infections of communicable viral diseases (CVDs) are posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of hospital-acquired infections, and subsequently higher rates of morbidity and mortality. We proposed a domain knowledge-driven infection risk model to quantify the individual HCP and the population-level healthcare facility risks. For individual-level risk estimation, a time-variant infection risk model is proposed to capture the transmission dynamics of CVDs. At the population-level, the infection risk is estimated using a Bayesian network model constructed from three feature sets including individual-level factors, engineering control factors, and administrative control factors. The sensitivity analyses indicated that the uncertainty in the individual infection risk can be attributed to two variables: the number of close contacts and the viral transmission probability. The model validation was implemented in the transmission probability model, individual level risk model, and population-level risk model using a Coronavirus disease case study. Regarding the first, multivariate logistic regression was applied for a cross-sectional data in the UK with an AIC value of 7317.70 and a 10-fold cross validation accuracy of 78.23%. For the second model, we collected laboratory-confirmed COVID-19 cases of HCP in different occupations. The occupation-specific risk evaluation suggested the highest-risk occupations were registered nurses, medical assistants, and respiratory therapists, with estimated risks of 0.0189, 0.0188, and 0.0176, respectively. To validate the population-level risk model, the infection risk in Texas and California was estimated. The proposed model will significantly influence the PPE allocation and safety plans for HCP Comment: 10 pages, 4 figures, Journal of Biomedical and Health Informatics |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2111.05761 |
| رقم الأكسشن: | edsarx.2111.05761 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |