Healthcare personnel (HCP) is facing a consistent risk of viral infections. We proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant model was proposed to capture the disease transmission dynamics. At the population-level, the infection risk was estimated using a Bayesian network model constructed from three feature sets. For model validation, we investigated the case study of the Coronavirus disease. The variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. We further validated the individual risk model by considering six occupations in the U.S. O*Net database. For the population-level risk model validation, the infection risk in Texas and California was estimated. The accurate estimation of infection risk will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies.