Infection Risk Assessment Research Articles

WTP3.13 Management of early complications of liver transplantation from a living related donor

Abstract Aim The aim of this study is to highlight the importance of the development of adequate surgical technique, the proper selection of donors, the preparation of recipients and the introduction of new molecules of Immunosuppressants in improving liver transplant (LT) outcomes. The study also shows that early complications, occurring within the first 3 months, are responsible for two-thirds of deaths. Methods All LT candidates were assessed for contraindications to immunosuppressants, social and psychiatric assessment, and operative and infectious risk assessment. Patients who received LT were followed according to a rigorous pre-established protocol that allowed the detection of early postoperative complications and surgical revision in some cases. Results Post-operative surgical complications occurred in 04 patients who required immediaterevision surgery for hemoperitoneum, portal thrombosis that required unblockage of the TP, thrombosis of the hepatic artery requiring new arterial anastomosis and liver resection, and one patient developed an infectious complication (MAT). Three patients experienced complications related to immunosuppressants. One patient required renal dialysis. Two patients developed biliary stenosis; treated endoscopically in one patient, and biliary repair after failure of endoscopic treatment in another. Conclusion Hemorrhage, primary graft non-function, multiple organ failure and infection, acute rejection and surgical complications dominate post-LT follow-up. Immunosuppressive treatments are responsible for a significant morbidity rate. Early detection of complications and their immediate management reducing morbidity and mortality. The living related donor LT program remains an option in our country due to the impossibility of LT as part of an ODB.

Komparasi Algoritma Machine Learning untuk Klasifikasi Gejala Coronavirus Disease 19 (Covid-19)

COVID-19 or Corona Virus Disease 19 is a member of the extended family of coronaviruses that cause a spectrum of illnesses from mild to severe, including MERS and SARS. While the cause of COVID-19 transmission has not been confirmed, it is believed that the virus is transmitted from animals to humans, causing various symptoms such as cough, runny nose, fever, sore throat and loss of smell. Research was conducted to classify COVID-19 symptoms into low, medium, and high categories in patients. This study aims to classify patient data and determine the risk of COVID-19 infection based on the severity of symptoms, namely mild, moderate, and high. Machine learning methods, including Decision Tree and SVM algorithms, are introduced and compared with K-Nearest Neighbor (K-NN), Neural Network (NN), Random Forest (RF), and Naive Bayes. The dataset used contains 127 patient records from kaggle.com. The test results showed that SVM achieved 54% accuracy, while Decision Tree achieved 98%. This research provides important insights into the risk assessment of COVID-19 infection based on symptom severity, and the use of machine learning techniques is expected to improve analysis and prediction capabilities in the face of the COVID-19 pandemic.

Camera-based occupant behavior trajectory capture and on-site assessment of spatio-temporal transmission risk of airborne virus in a university office building

Aerosol transmission is a route of contagion. The engineering field predicts risk in indoor environment by simulating the airborne transmission of the virus. However, occupant behavior is one of the main causes for the gap between predicted and actual airborne virus transmission risk. To improve the accuracy of transmission risk assessment, this study presents a framework for real-time assessment of infection risk based on the behavioral trajectory of the occupants captured by cameras. The study focused on analyzing the impact of short-range exposure and long-range exposure of occupants on the infection risk through fine-grained trajectories. On-site measurements were conducted in a university office building to obtain the air change rates (ACH) and occupant contact time respectively. A numerical model for quantifying the exposure risk of the two contact routes was also developed. The results demonstrated that the individual movement is diverse. The infection risk varies with the occupied space, behavioral patterns, and exposure time. The main passages and seats in the room, corridor corners, and room doorways are areas of the high frequency of short-range contact. The fraction of long-range exposure is 57.2%, indicating long-range exposure is the dominant route for the limited scenarios and population in this study. The cumulative risk of infection was 0.021 for teachers; 0.029 for employees; and 0.045 for students, with assumptions of initial infection rates of 5%. It can provide real-time warning for occupants through on-site monitoring and contribute to the transmission control and source tracing of airborne viruses.

A coupled experimental and statistical approach for an assessment of SARS-CoV-2 infection risk at indoor event locations

Indoor event locations are particularly affected by the SARS-CoV-2 pandemic. At large venues, only incomplete risk assessments exist, whereby no suitable measures can be derived. In this study, a physical and data-driven statistical model for a comprehensive infection risk assessment has been developed. At venues displacement ventilation concepts are often implemented. Here simplified theoretical assumptions fail for the prediction of relevant airflows for airborne transmission processes. Thus, with locally resolving trace gas measurements infection risks are computed more detailed. Coupled with epidemiological data such as incidences, vaccination rates, test sensitivities, and audience characteristics such as masks and age distribution, predictions of new infections (mean), situational R-values (mean), and individual risks on- and off-seat can be achieved for the first time. Using the Stuttgart State Opera as an example, the functioning of the model and its plausibility are tested and a sensitivity analysis is performed with regard to masks and tests. Besides a reference scenario on 2022-11-29, a maximum safety scenario with an obligation of FFP2 masks and rapid antigen tests as well as a minimum safety scenario without masks and tests are investigated. For these scenarios the new infections (mean) are 10.6, 0.25 and 13.0, respectively. The situational R-values (mean) – number of new infections caused by a single infectious person in a certain situation – are 2.75, 0.32 and 3.39, respectively. Besides these results a clustered consideration divided by age, masks and whether infections occur on-seat or off-seat are presented. In conclusion this provides an instrument that can enable policymakers and operators to take appropriate measures to control pandemics despite ongoing mass gathering events.

Window Opening or Mechanical Ventilation Systems for Italian Schools? Energetic Aspects, CO2 Concentration and Infection Risk Assessment Based on the SARS-CoV-2 Data

During the first COVID outbreak, schools were seen as a substantial issue regarding the spread of the disease, since schools are the most densely occupied indoor environment continuously over time. The infection risk of SARS-CoV-2 could reach large levels, especially in locations with inadequate ventilation rates, according to the current knowledge of airborne transmitted diseases. The first main drive to reduce viral concentration is dilution, provided by infiltration, air change through aeration (window opening), mechanical ventilation; the second drive is filtration. After a brief investigation of the prescriptions and requirements for ventilation in school rooms stipulated by Italian law, the present work compares the energy requirements for ventilation in two main cases—window opening (and infiltration) and mechanical ventilation—the effect of wearing or not wearing a mask is also considered. Afterward, the focus is shifted to the infection risk assessment, comparing the two different ventilation techniques as mentioned above. Not only is it found that window opening is far from an effective technique for controlling the spread of the disease; it is also found that for the same amount of thermal energy needed, mechanical ventilation can reduce the individual infection risk by a factor of 3, thus providing a better IAQ. It is also proved that mechanical ventilation, in terms of infection control, can not only replace but even outperform the effect of masks.

A framework for airborne infection risk assessment based on real-time occupant distribution prediction in outpatient waiting space

Outpatient waiting space (OWS) is a place with high risks of cross-infection and epidemic transmission of infectious respiratory diseases in hospitals. The dynamic changes of occupant distribution in OWS significantly increase the uncertainty of its infection risk assessment. Therefore, this study aims to develop an integrated framework for airborne infection risk assessment based on real-time occupant distribution prediction in OWS. The proposed framework included four steps. First, the consultation data and monitoring video data were collected from the consultation service system and monitoring videos. Second, difference analysis and correlation analysis were performed to identify the feature variables from the collected data for predicting occupant number (OPN) and their dwell time (DT) in OWS. Third, different machine learning (ML) models were trained using the extracted feature variables to select the model with the best prediction performance. Finally, based on the predicted OPN and DT, the real-time airborne infection risk was calculated. A typical children's hospital was selected to verify the proposed framework in 10 OWSs of different departments. The results showed that the average prediction accuracy for OPN and DT was 73.31% and 72.66%, respectively. The infection probability calculated based on the predicted occupant distribution was close to the actual values with a mean absolute error of 0.27, and its accuracy was improved by 64.9% compared with the calculation results using standard OPN and fixed DT. Furthermore, the application of this framework could reduce the ventilation rate by 11.36% compared with that using fixed ventilation according to the existing standards.

Seroprevalence and risk factors associated with bovine viral diarrhoea virus in Turkey.

Bovine viral diarrhoea virus (BVDV) is an infectious agent that is extensively observed worldwide and is among the crucial pathogens of the cattle industry. BVDV infection causes gastrointestinal and respiratory diseases. This study aimed to determine the seropositivity and associated potential risk factors for the risk assessment of BVDV infection in the dairy cattle population in the Aegean Region of Turkey. In this cross-sectional study, 552 serum samples were collected between August 2018 and September 2019 from 48 herds that were not vaccinated against BVDV. Farmers were interviewed using a questionnaire on potential BVDV risk factors. BVDV-specific antibodies in serum samples were detected using a commercial indirect-ELISA kit. The herd-level and animal-level prevalence of BVDV seropositivity was 89.58% (95% CI: 77.83-95.47) and 48.37% (95% CI: 44.23-52.54), respectively. The multivariable logistic regression model analysis demonstrated a positive correlation between BVDV infection and age, common pasture use with small ruminants, the use of natural insemination, and history of respiratory and/or reproduction problems. The results of this study showed that BVDV infection is common in dairy herds in the Aegean Region. The risk assessment would aid the implementation of a voluntary control programme for BVDV in this region.

552-P: Disparities in Preventive Reproductive Health Services in Women with Type 1 and Type 2 Diabetes

About 50% of (~13 million) reproductive aged women with Type I or II diabetes receive any preconception care. Even fewer receive contraceptive counseling, or other vital reproductive health services. This scoping review aimed to explore if reproductive aged women with diabetes receive adequate preventative reproductive health services compared to women without diabetes. A systematic search strategy of the published literature was built and applied in multiple databases (PubMed, Web of Science, Cochrane). Interventional and observational studies describing access to reproductive health services among reproductive-aged women with compared to those without diabetes were included. The Preferred Reporting Items for Scoping Reviews and Meta-Analyses (PRISMA) guidelines were followed. The search strategy identified 6785 unique records. A two reviewer method was used to screen abstracts, with 41 studies identified to be relevant. These studies were separated into the domains of contraception counseling (N=20), cervical cancer screening (N=12), counseling/screening for sexually transmitted infections (N=3), and preconception counseling (N=12). We did not include studies focusing on prenatal care, pregnancy, menstrual irregularities, sexual dysfunction, or breast cancer screening. While a vast amount of literature exists related to diabetes and pregnancy, this scoping review draws attention to the lack of research focused on the reproductive health of women with diabetes prior to pregnancy. The majority of available literature focuses on contraception and preconception management, consistently demonstrating that women with diabetes lack adequate preventative reproductive healthcare compared to their nondiabetic counterparts. Far less evidence exists for sexually transmitted infection risk assessment and screening. It is imperative that future research focus on ways to mitigate disparities in access to reproductive health services for women with diabetes. Disclosure M.Treasure: None. P.S.Nourbakhsh: None. A.Osuna-garcia: None. S.C.Esteves: None. L.N.Kransdorf: None. L.E.Wisk: None.

Does the exponential Wells-Riley model provide a good fit for human coronavirus and rhinovirus? A comparison of four dose-response models based on human challenge data.

The risk assessments during the COVID-19 pandemic were primarily based on dose-response models derived from the pooled datasets for infection of animals susceptible to SARS-CoV. Despite similarities, differences in susceptibility between animals and humans exist for respiratory viruses. The two most commonly used dose-response models for calculating the infection risk of respiratory viruses are the exponential and the Stirling approximated β-Poisson (BP) models. The modified version of the one-parameter exponential model or the Wells-Riley model was almost solely used for infection risk assessments during the pandemic. Still, the two-parameter (α and β) Stirling approximated BP model is often recommended compared to the exponential dose-response model due to its flexibility. However, the Stirling approximation restricts this model to the general rules of β≫1 and α≪β, and these conditions are very often violated. To refrain from these requirements, we tested a novel BP model by using the Laplace approximation of the Kummer hypergeometric function instead of the conservative Stirling approximation. The datasets of human respiratory airborne viruses available in the literature for human coronavirus (HCoV-229E) and human rhinovirus (HRV-16 and HRV-39) are used to compare the four dose-response models. Based on goodness-of-fit criteria, the exponential model was the best fitting model for the HCoV-229E (k=0.054) and for HRV-39 datasets (k=1.0), whereas the Laplace approximated BP model followed by the exact and Stirling approximated BP models are preferred for both the HRV-16 (α=0.152 and β=0.021 for Laplace BP) and the HRV-16 and HRV-39 pooled datasets (α=0.2247 and β=0.0215 for Laplace BP).

Have genetic targets for faecal pollution diagnostics and source tracking revolutionised water quality analysis yet?

The impacts on faecal pollution analysis using nucleic acid-based methods, such as PCR and sequencing, in health-related water quality research were assessed by rigorous literature analysis. A wide range of application areas and study designs has been identified since the first application more than 30 years ago (>1,100 publications). Given the consistency of methods and assessment types, we suggest defining this emerging part of science as a new discipline: genetic faecal pollution diagnostics (GFPD) in health-related microbial water quality analysis. Undoubtedly, GFPD has already revolutionised faecal pollution detection and microbial source tracking, the current core applications. GFPD is also expanding to many other research areas, including infection and health risk assessment, evaluation of microbial water treatment, and support of wastewater surveillance. In addition, storage of DNA extracts allows for biobanking, which opens up new perspectives. The tools of GFPD can be combined with cultivation-based standardised faecal indicator enumeration, pathogen detection, and various environmental data types, in an integrated data analysis approach. This comprehensive meta-analysis provides the scientific status quo of this field, including trend analyses and literature statistics, outlining identified application areas, and discussing the benefits and challenges of nucleic acid-based analysis in GFPD.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO2 concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%–57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Numerical investigation of airborne infection risk in an elevator cabin under different ventilation designs

Airborne transmission of SARS-CoV-2 via virus-laden aerosols in enclosed spaces poses a significant concern. Elevators, commonly utilized enclosed spaces in modern tall buildings, present a challenge as the impact of varying heating, ventilation, and air conditioning (HVAC) systems on virus transmission within these cabins remains unclear. In this study, we employ computational modeling to examine aerosol transmission within an elevator cabin outfitted with diverse HVAC systems. Using a transport equation, we model aerosol concentration and assess infection risk distribution across passengers' breathing zones. We calculate the particle removal efficiency for each HVAC design and introduce a suppression effect criterion to evaluate the effectiveness of the HVAC systems. Our findings reveal that mixing ventilation, featuring both inlet and outlet at the ceiling, proves most efficient in reducing particle spread, achieving a maximum removal efficiency of 79.40% during the exposure time. Conversely, the stratum ventilation model attains a mere removal efficiency of 3.97%. These results underscore the importance of careful HVAC system selection in mitigating the risk of SARS-CoV-2 transmission within elevator cabins.

Moral Judgments Impact Perceived Risks From COVID-19 Exposure

The COVID-19 pandemic created enormously difficult decisions for individuals trying to navigate both the risks of the pandemic and the demands of everyday life. Good decision making in such scenarios can have life and death consequences. For this reason, it is important to understand what drives risk assessments during a pandemic, and to investigate the ways that these assessments might deviate from ideal risk assessments. In a preregistered online study of U.S. residents (N = 841) using two blocks of vignettes about potential COVID exposure scenarios, we investigated the effects of moral judgment, importance, and intentionality on COVID infection risk assessments. Results demonstrate that risk judgments are sensitive to factors unrelated to the objective risks of infection. Specifically, activities that are morally justified are perceived as safer while those that might subject people to blame or culpability, are seen as riskier, even when holding objective risk fixed. Similarly, unintentional COVID exposures are judged as safer than intentional COVID exposures. While the effect sizes are small, these findings may have implications for public health and risk communications, particularly if public health officials are themselves subject to these biases.

Detection of specific RBD+ IgG+ memory B cells by flow cytometry in healthcare workers and patients with inborn errors of immunity after BNT162b2 m RNA COVID-19 vaccination.

Inborn errors of immunity (IEI) are a heterogeneous group of diseases caused by intrinsic defects of the immune system. Estimating the immune competence of immunocompromised patients for an infection risk assessment or after SARS-CoV-2 vaccination constituted a challenge. The aim of this study was to determine the humoral responses of patients with IEI through a comprehensive analysis of specific receptor-binding domain-positive (RBD+) IgG+ memory B cells (MBCs) by flow cytometry, together with routine S-specific IgG antibodies and QuantiFERON SARS-CoV-2 (T-cell response), before the vaccine and 3 weeks after a second dose. We first analyzed the percentage of specific RBD+ IgG+ MBCs in healthy healthcare workers. Within the control group, there was an increase in the percentage of specific IgG+ RBD+ MBCs 21 days after the second dose, which was consistent with S-specific IgG antibodies.Thirty-one patients with IEI were included for the pre- and post-vaccination study; IgG+ RBD+ MBCs were not evaluated in 6 patients due to an absence of B cells in peripheral blood. We detected various patterns among the patients with IEI with circulating B cells (25, 81%): an adequate humoral response was observed in 12/25, consider by the detection of positive S-specific IgG antibodies and the presence of specific IgG+ RBD+ MBCs, presenting a positive T-cell response; in 4/25, very low S-specific IgG antibody counts correlated with undetectable events in the IgG+ RBD+ MBC compartment but with positive cellular response. Despite the presence of S-specific IgG antibodies, we were unable to detect a relevant percentage of IgG+ RBD+ MBCs in 5/25; however, all presented positive T-cell response. Lastly, we observed a profound failure of B and T-cell response in 3 (10%) patients with IEI, with no assessment of S-specific IgG antibodies, IgG+ RBD+ MBCs, and negative cellular response. The identification of specific IgG+ RBD+ MBCs by flow cytometry provides information on different humoral immune response outcomes in patients with IEI and aids the assessment of immune competence status after SARS-CoV-2 mRNA vaccine (BNT162b2), together with S-specific IgG antibodies and T-cell responses.

Risk assessment of human mpox infections: retrospective cohort study

ObjectivesThe global supply of vaccines against mpox (previously called monkeypox virus infection) was significantly lower than the demand. Therefore, evidence-based vaccine prioritization criteria, based on risk assessment were needed. Our objective was therefore to identify the characteristics of individuals at the highest risk for mpox. MethodsThis population-based cohort study included all Clalit Health Services (CHS) subjects assumed to be at risk for mpox. The eligibility criteria for inclusion were determined based on known characteristics of people with infection worldwide and insights of lesbian, gay, bisexual, transgender, queer+ (LGBTQ+) -specialized CHS clinicians. Cox hazards models were used to identify the risk factors for mpox within the study cohort. The study commenced on 6 June 2022, the date of the first known mpox in CHS members, until 31 July 2022, when the mpox vaccination campaign started. ResultsA total of 8088 individuals of 4.7 million CHS members (0.18%) were identified according to the study inclusion criteria. Of those, 69 (0.85%) developed infection during the study period. Risk factors for mpox were birth in 1980 or later (hazard ratio, 5.04; 95% CI, 2.11–12.02), history of syphilis (2.62; 1.58–4.35), registration to primary healthcare clinics in the Tel Aviv district (2.82; 1.44–5.54), HIV–pre-exposure prophylaxis medication use (3.96; 2.14–7.31), PDE5 inhibitors use (2.92; 1.77–4.84), and recent sexually transmitted infections (STIs) within the last 18 months (2.27; 1.35–3.82). No infections were observed in individuals with none of the factors. Individuals with three or more risk factors had a 20.30-fold (10.39–39.69) higher risk for mpox compared with those with 0–2, with 85.5% (75.0–92.8%) sensitivity and 77.8% (76.9–78.7%) specificity. DiscussionWeighting individuals' risk levels based on validated risk factors against vaccine availability can assist health systems in the equitable prioritization of vaccine allocation in various future outbreaks, given supply-demand gaps.

Onsite measurement and dilution-based infection risk assessment for a new mobile negative pressure isolation room (MNPIR) as a solution to minimize cross-infection

This study proposes an onsite measurement protocol and infection risk assessment using tracer gas tests based on the dilution-based method to evaluate the ventilation system's performance in the mobile negative pressure isolation room (MNPIR) and minimize infection risk for health care workers (HCW). The dilution-based infection risk assessment is better in determining spatial distribution of infection risk compared to the Wells-Riley model. The study shows that ventilation effectiveness to reduce the infection risk for HCWs can be reviewed using four onsite measurement protocols and a dilution-based infection risk assessment. Optimizing the SA and EA installation locations is key to reducing the cross-infection risk of HCWs, and the contaminant removal effectiveness (CRE) index averaged 2.6, indicating adequate ventilation efficiency. By applying an appropriate ventilation system, the infection risk in HCW spaces was reduced to 50% or less of the average risk (by the Wells-Riley model). The proposed approach allows for quantitative evaluation of infection risk by location and exposure time, demonstrating that the MNPIR can effectively minimize infection risk for HCWs. The study suggests follow-up research on cooling and heating strategies that consider both patient comfort and effective ventilation performance in the MNPIR.

ASSESSMENT OF INFECTION RISK IN OLDER PATIENTS WITH PSORIASIS ON SYSTEMIC THERAPY

Systemic treatment for psoriasis can be effective but have more risk of infection especially in old age groups. The aim of this study is to assess the infection risk in older patients with psoriasis on systemic therapy. A total of 639 patients were included in the study those were taking systemic therapy for psoriasis for last 6 months. They were divided into two groups. Group A with younger patients whereas the Group B had the older patients. Data was gathered and analyzed. Both groups were compared with respect to the incidence of infection. The results showed that that the group B had significantly high frequency of infection compared to the Group A. In conclusion, the assessment of infection risk in older patients with psoriasis who are receiving systemic therapies is crucial to ensure that patients receive the most appropriate and effective treatment while minimizing the risk of infections and other adverse events.

Assessment of respiratory disease infection risk and natural ventilation intervention countermeasures in teaching spaces: A campus case study

The risk of indoor respiratory disease transmission can be significantly reduced through interventions that target the built environment. Several studies have successfully developed theoretical models to calculate the effects of built environment parameters on infection rates. However, current studies have mainly focused on calculating infection rate values and comparing pre- and post-optimization values, lacking a discussion of safe baseline values for infection rates with risk class classification. The purpose of this paper is to explore the design of interventions in the built environment to improve the ability of buildings to prevent virus transmission, with a university campus as an example. The study integrates the Wells-Riley model and basic reproduction number to identify teaching spaces with high infection risk on campus and proposes targeted intervention countermeasures based on the analysis of critical parameters. The results showed that teaching buildings with a grid layout pattern had a higher potential risk of infection under natural ventilation. By a diversity of building environment interventions designed, the internal airflow field of classrooms can be effectively organized, and the indoor virus concentration can be reduced. We can find that after optimizing the building mentioned above and environment intervention countermeasures, the maximum indoor virus infection probability can be reduced by 22.88%, and the basic reproduction number can be reduced by 25.98%, finally reaching a safe level of less than 1.0. In this paper, we support university campuses' respiratory disease prevention and control programs by constructing theoretical models and developing parametric platforms.

CIRO: COVID-19 infection risk ontology.

Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person's infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment.

Development of a Tool to Measure Compliance with Infection Prevention Activities Against Emerging Respiratory Infectious Diseases among Nurses Working in Acute Care and Geriatric Hospitals.

This study developed a preliminary instrument to measure nurses' infection prevention compliance against emerging respiratory infectious diseases and to verify the reliability and validity of the developed instrument. The participants were 199 nurses working at a university hospital with more than 800 beds and two long-term care hospitals. Data were collected in May 2022. The final version of the developed instrument consisted of six factors and 34 items, with an explanatory power of 61.68%. The six factors were equipment and environment management and education, hand hygiene and respiratory etiquette, infection risk assessment and flow management, protection of employees in contact with infected patients, ward access management of patients with infectious diseases, and wearing and removing personal protective equipment. We verified the convergent and discriminant validities of these factors. The instrument's internal consistency was adequate (Cronbach's α=0.82), and the Cronbach's α of each factor ranged from 0.71 to 0.91. This instrument can be utilized to determine the level of nurses' compliance with infection prevention activity against emerging respiratory infectious diseases and will contribute to measuring the effectiveness of future programs promoting infection-preventive activities.