Who acquires infection from whom? A sensitivity analysis of transmission dynamics during the early phase of the COVID-19 pandemic in Belgium

Age-related heterogeneity in a host population, whether due to how individuals mix and contact each other, the nature of host-pathogen interactions defining epidemiological parameters, or demographics, is crucial in studying infectious disease dynamics. Compartmental models represent a popular approach to address the problem, dividing the population of interest into a discrete and finite number of states depending on, for example, individuals' age and stage of infection. We study the corresponding linearised system whose operator, in the context of a discrete-time model, equates to a square matrix known as the next generation matrix. Performing formal perturbation analysis of the entries of the aforementioned matrix, we derive indices to quantify the age-specific variation of its dominant eigenvalue (i.e., the reproduction number) and explore the relevant epidemiological information we can derive from the eigenstructure of the matrix. The resulting method enables the assessment of the impact of age-related population heterogeneity on virus transmission. In particular, starting from an age-structured SEIR model, we demonstrate the use of this approach for COVID-19 dynamics in Belgium. We analyse the early stages of the SARS-CoV-2 spread, with particular attention to the pre-pandemic framework and the lockdown lifting phase initiated as of May 2020. Our results, influenced by our assumption on age-specific susceptibility and infectiousness, support the hypothesis that transmission was only influenced to a small extent by children in the age group [0,18) and adults over 60 years of age during the early phases of the pandemic and up to the end of July 2020.

Patient safety in Work Environments: Perceptions of Pediatric Healthcare Providers in Taiwan

BACKGROUND

Extensive research on the link between the organizational characteristics of the work environment and patient safety in a healthcare organization has been conducted; yet, only a few studies have concentrated on care providers in a pediatric unit.

OBJECTIVES

To determine the correlation between different work environment factors impacting patient safety in a pediatric care unit from the perspective of registered nurses working in these units.

DESIGN

Cross-sectional design.

DATA SOURCES AND METHODS

The study was conducted with 155 registered nurses from a pediatric unit in a medical center in Taiwan with the Chinese version of the Safety Attitudes Questionnaire (SAQ) 2014-2017.

RESULTS

Teamwork climate, higher job satisfaction, and better working conditions are linked to positive perceptions of patient safety culture. Emotional exhaustion is negatively related to most dimensions of patient safety.

CONCLUSION

Teamwork climate, job satisfaction, working conditions, and emotional exhaustion were identified as critical factors impacting the patient safety climate.

IMPLICATIONS FOR NURSING OR HEALTH POLICY

Investments to improve teamwork climate, job satisfaction, and working conditions and reduce emotional exhaustion may have a positive effect on patient safety in pediatric care units.

The role of specialized hospital units in infection and mortality risk reduction among patients with hematological cancers

MOTIVATION

Patients with hematological malignancies are susceptible to life-threatening infections after chemotherapy. The current study aimed to evaluate whether management of such patients in dedicated inpatient and emergency wards could provide superior infection prevention and outcome.

METHODS

We have developed an approach allowing to retrieve infection-related information from unstructured electronic medical records of a tertiary center. Data on 2,330 adults receiving 13,529 chemotherapy treatments for hematological malignancies were identified and assessed. Infection and mortality hazard rates were calculated with multivariate models. Patients were randomly divided into 80:20 training and validation cohorts. To develop patient-tailored risk-prediction models, several machine-learning methods were compared using area under the curve (AUC).

RESULTS

Of the tested algorithms, the probit model was found to most accurately predict the evaluated hazards and was implemented in an online calculator. The infection-prediction model identified risk factors for infection based on patient characteristics, treatment and history. Observation of patients with a high predicted infection risk in general wards appeared to increase their infection hazard (p = 0.009) compared to similar patients observed in hematology units. The mortality-risk model demonstrated that for infection events starting at home, admission through hematology services was associated with a lower mortality hazard compared to admission through the general emergency department (p = 0.007). Both models show that dedicated hematological facilities and emergency services improve patient outcome post-chemotherapy. The calculated numbers needed to treat were 30.27 and 31.08 for the dedicated emergency and observation facilities, respectively. Infection hazard risks were found to be non-monotonic in time.

CONCLUSIONS

The accuracy of the proposed mortality and infection risk-prediction models was high, with the AUC of 0.74 and 0.83, respectively. Our results demonstrate that temporal assessment of patient risks is feasible. This may enable physicians to move from one-point decision-making to a continuous dynamic observation, allowing a more flexible and patient-tailored admission policy.

Research on complex dynamic behavior control of supply chain finance nonlinear system based on fractional differential operators

Supply chain management is a kind of behavior for enterprises to create core competitiveness in a complex competitive environment, especially for supply chain finance, which is a typical complex system with dynamic, open, and emergent non-linear characteristics in structure, environment, and behavior. For fractional order non-linear multi-agent systems, the event trigger control protocol is studied, and an event based on the threshold function of decreasing time is designed. According to the properties of the fractional linear differential equations, the consistency criterion is obtained, and the Zeno phenomenon is effectively avoided. Chaos exists in fractional-order electronic oscillators with order lower than 4th order, and the lowest order of chaos in the fractional-order system is only 2.3 orders. The research shows that under the appropriate parameter adjustment, chaos exists in the fractional-order vanderPol oscillator with order less than 4th order, and the lowest order of chaos in the fractional-order system is 2.7.