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Hospital-acquired infections of communicable viral diseases (CVDs) are posinga tremendous challenge to healthcare workers globally. Healthcare personnel(HCP) is facing a consistent risk of hospital-acquired infections, andsubsequently higher rates of morbidity and mortality. We proposed a domainknowledge-driven infection risk model to quantify the individual HCP and thepopulation-level healthcare facility risks. For individual-level riskestimation, a time-variant infection risk model is proposed to capture thetransmission dynamics of CVDs. At the population-level, the infection risk isestimated using a Bayesian network model constructed from three feature setsincluding individual-level factors, engineering control factors, andadministrative control factors. The sensitivity analyses indicated that theuncertainty in the individual infection risk can be attributed to twovariables: 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 aCoronavirus disease case study. Regarding the first, multivariate logisticregression was applied for a cross-sectional data in the UK with an AIC valueof 7317.70 and a 10-fold cross validation accuracy of 78.23%. For the secondmodel, we collected laboratory-confirmed COVID-19 cases of HCP in differentoccupations. The occupation-specific risk evaluation suggested the highest-riskoccupations were registered nurses, medical assistants, and respiratorytherapists, with estimated risks of 0.0189, 0.0188, and 0.0176, respectively.To validate the population-level risk model, the infection risk in Texas andCalifornia was estimated. The proposed model will significantly influence thePPE allocation and safety plans for HCP

A Probabilistic Domain-knowledge Framework for Nosocomial Infection Risk  Estimation of Communicable Viral Diseases in Healthcare Personnel: A Case  Study for COVID-19