Nursing students' experiences of virtual simulation when using a video conferencing system - a mixed methods study.

OBJECTIVES: There is limited knowledge about students' experiences with virtual simulation when using a video conferencing system. Therefore, the aim of this study was to explore how second-year undergraduate nursing students experienced learning through virtual simulations during the COVID-19 pandemic. METHODS: The study had an exploratory design with both quantitative and qualitative approaches. In total, 69 nursing students participated in two sessions of virtual simulation during spring 2020, and 33 students answered online questionnaires at session 1. To further explore students' experiences, one focus group interview and one individual interview were conducted using a video conferencing system after session 2. In addition, system information on use during both sessions was collected. RESULTS: Changes in the students' ratings of their experiences of virtual simulation with the Body Interact™ system were statistically significant. The virtual simulation helped them to bridge gaps in both the teaching and learning processes. Four important aspects of learning were identified: 1) learning by self-training, 2) learning from the software (Body Interact™), 3) learning from peers, and 4) learning from faculty. CONCLUSIONS: We conclude that virtual simulation through a video conferencing system can be useful for student learning and feedback from both peers and faculty is important.

Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics.

As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate [Formula: see text] is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, [Formula: see text]) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, [Formula: see text]) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with [Formula: see text] hovering around its threshold value.

Drug repurposing: Iron in the fire for older drugs.

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

A systematic review of low-cost simulators in ENT surgery.

BACKGROUND: Simulation training has become a key part of the surgical curriculum over recent years. Current trainees face significantly reduced operating time as a result of the coronavirus disease 2019 pandemic, alongside increased costs to surgical training, thus creating a need for low-cost simulation models. METHODS: A systematic review of the literature was performed using multiple databases. Each model included was assessed for the ease and expense of its construction, as well as its validity and educational value. RESULTS: A total of 18 low-cost simulation models were identified, relating to otology, head and neck surgery, laryngeal surgery, rhinology, and tonsil surgery. In only four of these models (22.2 per cent) was an attempt made to demonstrate the educational impact of the model. Validation was rarely formally assessed. CONCLUSION: More efforts are required to standardise validation methods and demonstrate the educational value of the available low-cost simulation models in otorhinolaryngology.

Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study.

BACKGROUND: COVID-19 outbreaks have occurred in homeless shelters across the US, highlighting an urgent need to identify the most effective infection control strategy to prevent future outbreaks. METHODS: We developed a microsimulation model of SARS-CoV-2 transmission in a homeless shelter and calibrated it to data from cross-sectional polymerase chain reaction (PCR) surveys conducted during COVID-19 outbreaks in five homeless shelters in three US cities from March 28 to April 10, 2020. We estimated the probability of averting a COVID-19 outbreak when an exposed individual is introduced into a representative homeless shelter of 250 residents and 50 staff over 30 days under different infection control strategies, including daily symptom-based screening, twice-weekly PCR testing, and universal mask wearing. RESULTS: The proportion of PCR-positive residents and staff at the shelters with observed outbreaks ranged from 2.6 to 51.6%, which translated to the basic reproduction number (R0) estimates of 2.9-6.2. With moderate community incidence (~ 30 confirmed cases/1,000,000 people/day), the estimated probabilities of averting an outbreak in a low-risk (R0 = 1.5), moderate-risk (R0 = 2.9), and high-risk (R0 = 6.2) shelter were respectively 0.35, 0.13, and 0.04 for daily symptom-based screening; 0.53, 0.20, and 0.09 for twice-weekly PCR testing; 0.62, 0.27, and 0.08 for universal masking; and 0.74, 0.42, and 0.19 for these strategies in combination. The probability of averting an outbreak diminished with higher transmissibility (R0) within the simulated shelter and increasing incidence in the local community. CONCLUSIONS: In high-risk homeless shelter environments and locations with high community incidence of COVID-19, even intensive infection control strategies (incorporating daily symptom screening, frequent PCR testing, and universal mask wearing) are unlikely to prevent outbreaks, suggesting a need for non-congregate housing arrangements for people experiencing homelessness. In lower-risk environments, combined interventions should be employed to reduce outbreak risk.

An Epidemiological Model Considering Isolation to Predict COVID-19 Trends in Tokyo, Japan: Numerical Analysis.

BACKGROUND: COVID-19 currently poses a global public health threat. Although Tokyo, Japan, is no exception to this, it was initially affected by only a small-level epidemic. Nevertheless, medical collapse nearly happened since no predictive methods were available to assess infection counts. A standard susceptible-infectious-removed (SIR) epidemiological model has been widely used, but its applicability is limited often to the early phase of an epidemic in the case of a large collective population. A full numerical simulation of the entire period from beginning until end would be helpful for understanding COVID-19 trends in (separate) counts of inpatient and infectious cases and can also aid the preparation of hospital beds and development of quarantine strategies. OBJECTIVE: This study aimed to develop an epidemiological model that considers the isolation period to simulate a comprehensive trend of the initial epidemic in Tokyo that yields separate counts of inpatient and infectious cases. It was also intended to induce important corollaries of governing equations (ie, effective reproductive number) and equations for the final count. METHODS: Time-series data related to SARS-CoV-2 from February 28 to May 23, 2020, from Tokyo and antibody testing conducted by the Japanese government were adopted for this study. A novel epidemiological model based on a discrete delay differential equation (apparent time-lag model [ATLM]) was introduced. The model can predict trends in inpatient and infectious cases in the field. Various data such as daily new confirmed cases, cumulative infections, inpatients, and PCR (polymerase chain reaction) test positivity ratios were used to verify the model. This approach also derived an alternative formulation equivalent to the standard SIR model. RESULTS: In a typical parameter setting, the present ATLM provided 20% less infectious cases in the field compared to the standard SIR model prediction owing to isolation. The basic reproductive number was inferred as 2.30 under the condition that the time lag T from infection to detection and isolation is 14 days. Based on this, an adequate vaccine ratio to avoid an outbreak was evaluated for 57% of the population. We assessed the date (May 23) that the government declared a rescission of the state of emergency. Taking into consideration the number of infectious cases in the field, a date of 1 week later (May 30) would have been most effective. Furthermore, simulation results with a shorter time lag of T=7 and a larger transmission rate of α=1.43α0 suggest that infections at large should reduce by half and inpatient numbers should be similar to those of the first wave of COVID-19. CONCLUSIONS: A novel mathematical model was proposed and examined using SARS-CoV-2 data for Tokyo. The simulation agreed with data from the beginning of the pandemic. Shortening the period from infection to hospitalization is effective against outbreaks without rigorous public health interventions and control.

The Impact of COVID-19 on Plastic Surgery Residency Training.

Nowadays didactic and surgical activities for residents in the surgery field are less and less due to an increasing burden of documentation and "non-educational work." Considering the current lockdown due to the COVID-19 pandemic, it has never been so important to find different ways to allow residents to improve their knowledge. We asked all plastic and esthetic surgery residents in our country to fill out a questionnaire to investigate changes in their didactical activity and analyze problems about their professional growth in the last few months. From the results of such questionnaires, we found that most of the residents feel the decrease in surgical activities during this time is a detrimental factor for their training and that even if all the schools have changed their didactical activities no school has introduced the use of virtual simulators to compensate for the decrease in surgical practice. Actually, the majority of residents use webinars to keep updated, stating that such technologies are useful but not sufficient to analyze plastic surgery topics in depth during COVID-19 lockdown. Virtual interactive tools are well known in different clinical and surgical specialties, and they are considered as a valid support, but it seems that in plastic surgery they are not so used. According to the most recent studies about residents' didactical program, we have investigated the potential of Anatomage Table in combination with Touch Surgery application as physical and mental aids to bypass the decreased number and kind of surgical interventions performed in this particular time. Anatomage is an academic user-friendly touch screen table; it is used by both medical students and residents to learn human anatomy and to master surgical anatomy. Touch Surgery is an application available on smartphones and tablets that gives the possibility to watch real and virtually designed surgical videos, accompanied by explanatory comments on the surgical phases; they are interactive and give the possibility to check what you have learned through tests administered after virtual classes. In our opinion, these tools represent reliable solutions to improve plastic residents' training, mostly during the COVID-19 pandemic. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .