Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, United States of America.
Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, North Dakota, United States of America.
PLoS One. 2022 Nov 21;17(11):e0272919. doi: 10.1371/journal.pone.0272919. eCollection 2022.
Hospital-acquired infections of communicable viral diseases (CVDs) have been posing a tremendous challenge to healthcare workers globally. Healthcare personnel (HCP) is facing a consistent risk of viral 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 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. For model validation, we investigated the case study of the Coronavirus disease, in which the individual-level and population-level infection risk models were applied. The data were collected from various sources such as COVID-19 transmission databases, health surveys/questionaries from medical centers, U.S. Department of Labor databases, and cross-sectional studies.
Regarding the individual-level risk model, 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. Next, the disease transmission probability was computed using a multivariate logistic regression applied for a cross-sectional HCP data in the UK, with the 10-fold cross-validation accuracy of 78.23%. Combined with the previous result, we further validated the individual infection risk model by considering six occupations in the U.S. Department of Labor O*Net database. The occupation-specific risk evaluation suggested that the registered nurses, medical assistants, and respiratory therapists were the highest-risk occupations. For the population-level risk model validation, the infection risk in Texas and California was estimated, in which the infection risk in Texas was lower than that in California. This can be explained by California's higher patient load for each HCP per day and lower personal protective equipment (PPE) sufficiency level.
The accurate estimation of infection risk at both individual level and population levels using our domain-knowledge-driven infection risk model will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies.
医院获得性传染病（CVDs）一直对全球医疗工作者构成巨大挑战。医疗保健人员（HCP）面临持续的病毒感染风险，因此发病率和死亡率更高。
我们提出了一种基于领域知识的感染风险模型，以量化个体 HCP 和人群水平的风险。对于个体水平的风险估计，提出了一个时变感染风险模型来捕捉 CVD 的传播动态。在人群水平上，使用从个体因素、工程控制因素和行政控制因素三个特征集构建的贝叶斯网络模型来估计感染风险。为了验证模型，我们研究了冠状病毒疾病的案例，在该案例中应用了个体水平和人群水平的感染风险模型。数据来自 COVID-19 传播数据库、医疗中心的健康调查/问卷、美国劳工部数据库和横断面研究等各种来源。
对于个体水平的风险模型，基于方差的敏感性分析表明，风险估计的不确定性归因于两个变量：密切接触者的数量和病毒传播概率。接下来，使用应用于英国横断面 HCP 数据的多元逻辑回归计算疾病传播概率，10 折交叉验证的准确率为 78.23%。结合之前的结果，我们通过考虑美国劳工部 O*Net 数据库中的六个职业进一步验证了个体感染风险模型。职业特定风险评估表明，注册护士、医疗助理和呼吸治疗师是风险最高的职业。对于人群水平的风险模型验证，估计了德克萨斯州和加利福尼亚州的感染风险，其中德克萨斯州的感染风险低于加利福尼亚州。这可以用加利福尼亚州每位 HCP 每天的患者负担更高和个人防护设备（PPE）充足水平更低来解释。
使用我们基于领域知识的感染风险模型准确估计个体和人群水平的感染风险，将显著增强 PPE 分配、HCP 安全计划和医院人员配备策略。
