Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Probabilistic Domain-knowledge Framework for Nosocomial Infection Risk
  Estimation of Communicable Viral Diseases in Healthcare Personnel: A Case
  Study for COVID-19   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

26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex