Modeling and simulation of infectious diseases: Microscale transmission, decontamination and macroscale propagation

The COVID-19 pandemic that started in 2019-2020 has led to a gigantic increase in modeling and simulation of infectious diseases. There are numerous topics associated with this epoch-changing event, such as (a) disease propagation, (b) transmission, (c) decontamination, and (d) vaccines. This is an evolving field. The targeted objective of this book is to expose researchers to key topics in this area, in a very concise manner. The topics selected for discussion have evolved with the progression of the pandemic. Beyond the introductory chapter on basic mathematics, optimization, and machine learning, the book covers four themes in modeling and simulation infectious diseases, specifically: Part 1: Macroscale disease propagation, Part 2: Microscale disease transmission and ventilation system design, Part 3: Ultraviolet viral decontamination, and Part 4: Vaccine design and immune response. It is important to emphasize that the rapid speed at which the simulations operate makes the presented computational tools easily deployable as digital twins, i.e., digital replicas of complex systems that can be inexpensively and safely optimized in a virtual setting and then used in the physical world afterward, thus reducing the costs of experiments and also accelerating development of new technologies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.

Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies

Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.

Literature Review of Statistical Methods Comparisons through Simulations When Using External Control Arm for Regulatory or HTA Submissions

Objectives: Using a historical control or external control arm (ECA) to augment or replace a concurrent control arm in a randomized trial is a hot topic given the challenge of patient recruitment in rare diseases or during COVID-19 pandemic. The FDA released draft guidance in 2021 on effectiveness and safety submissions using real-world evidence. While the guidance focuses mainly on elements of study design and data source selection, there is a lack of consensus in the selection of appropriate statistical methods when constructing an ECA. This study discusses rigorous statistical methodology for ECA-supported trials in regulatory or HTA submissions. Method(s): Targeted literature reviews of statistical simulations comparing methods for ECA in statistical journals were performed. The articles compared commonly used ECA-construction and analysis methods were selected and summarized, including but not limited to propensity score (PS)-based matching, weighting, and stratification, and PS plus Bayesian integrated approaches. Result(s): Type I error, power, bias, and coverage probability are common criteria used to compare different methods. When imbalances only exist in known baseline covariates and the outcome distributions are the same between the trial concurrent control and ECA, the PS method alone or paired with commensurate prior yield almost unbiased estimates, good Type I errors, and coverage probability. PS plus Bayesian approaches have wider interval width and lower power compared with PS-only methods. When there is a change in the outcome distribution over time, the PS (matching or IPTW) and commensurate prior integrated methods yield the smallest biases among all methods. Conclusion(s): PS and Bayesian integrated methods outperformed the PS-only methods in terms of bias and Type I error when outcome distribution changed with current trial control. A "sweet spot" that balances all criteria through trial-specific simulations could provide the ideal setting of trial analyses plan based on specific trial design and scenarios.Copyright © 2023

Detection of Multiple Structural Breaks in Large Covariance Matrices

This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An package "” is provided to implement the proposed algorithms.

COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem

This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

Crystal structure, DFT, molecular docking and dynamics simulation studies of 4,4-dimethoxychalcone

In the current study, the compound 4,4-dimethoxychalcone (DMC) was structurally studied and analyzed by in silico approach against Mpro to investigate its inhibitory potential. The molecular structure of the compound was confirmed by the single crystal X-ray diffraction studies. The crystal structure packing is characterized by various hydrogen bonds, C-H…π and π…π stacking. Intermolecular interactions are quantified by Hirshfeld surface analysis and the electronic structure was optimized by DFT calculations;results are in agreement with the experimental studies. Further, DMC was virtually screened against SARS-CoV-2 main protease (PDB-ID: 6LU7) using molecular docking, and molecular dynamics (MD) simulations to identify its inhibitory potential. A significant binding affinity exists between DMC and Mpro with a-6.00 kcal/mol binding energy. A MD simulation of 30ns was carried out;the results predict DMC possessing strong binding affinity and hydrogen-bonding interactions within the active site during the simulation period. Therefore, based on the results of the current investigation, it can be inferred that a DMC molecule may be able to inhibit Mpro of COVID-19. © 2023 by the authors;licensee Growing Science, Canada.

Simulation Analysis of COVID-19 Epidemic Model in Wuhan Based on SIR Model

Based on SIR model, combined with the mode of COVID-19 epidemic spread in Wuhan, the SIR model of COVID-19 epidemic spread is constructed, which mainly includes three aspects: simulation of the average number of infected people in COVID-19, simulation of cross-infection in COVID-19 and simulation of contact infection in COVID-19. Using the results of these three simulations, we can predict the spread of COVID-19 epidemic in the region, and find out the methods to prevent and control the outbreak or spread of the epidemic. © 2023 SPIE.

Simulation Study on Airflow Organization and Environment in Reconstructed Fangcang Shelter Hospital Based on CFD

With frequent outbreaks of COVID-19, the rapid and effective construction of large-space buildings into Fangcang shelter hospitals has gradually become one of the effective means to control the epidemic. Reasonable design of the ventilation system of the Fangcang shelter hospital can optimize the indoor airflow organization, so that the internal environment can meet the comfort of patients and at the same time can effectively discharge pollutants, which is particularly important for the establishment of the Fangcang shelter hospital. In this paper, through the reconstruction of a large-space gymnasium, CFD software is used to simulate the living environment and pollutant emission efficiency of the reconstructed Fangcang shelter hospital in summer under different air supply temperatures, air supply heights and exhaust air volume parameters. The results show that when the air supply parameters are set to an air supply height of 4.5 m, an air supply temperature of 18 °C, and an exhaust air volume of a single bed of 150 m3/h, the thermal comfort can reach level I, and the ventilation efficiency for pollutants can reach 69.6%. In addition, the ventilation efficiency is 70.1% and 70.3% when the exhaust air volume of a single bed is continuously increased to 200 and 250 m3/h, which can no longer effectively improve the pollutant emission and will cause an uncomfortable blowing feeling to patients. © 2023 by the authors.

GPU libraries speed performance analysis for RCWA simulation matrix operations

Rigorous Coupled Wave Analysis (RCWA) method is highly efficient for the simulation of diffraction efficiency and field distribution patterns in periodic structures and textured optoelectronic devices. GPU has been increasingly used in complex scientific problems such as climate simulation and the latest Covid-19 spread model. In this paper, we break down the RCWA simulation problem to key computational steps (eigensystem solution, matrix inversion/multiplication) and investigate speed performance provided by optimized linear algebra GPU libraries in comparison to multithreaded Intel MKL CPU library running on IRIDIS 5 supercomputer (1 NVIDIA v100 GPU and 40 Intel Xeon Gold 6138 cores CPU). Our work shows that GPU outperforms CPU significantly for all required steps. Eigensystem solution becomes 60% faster, Matrix inversion improves with size achieving 8x faster for large matrixes. Most significantly, matrix multiplication becomes 40x faster for small and 5x faster for large matrix sizes. © 2023 SPIE.

Impacts of COVID-19 on proton therapy - Shaping the future of patient selection

Objectives: This scoping review aimed to determine whether the COVID-19 pandemic influenced any modifications to patient selection methods or prioritisation and services provided by proton therapy centres. Method(s): This review was conducted based on the PRISMA methodology and Joanna Briggs Institute scoping review guidelines.1,2 A literature search was performed in Medline, Embase, Web Of Science and Scopus as well as grey literature. Keywords including "COVID-19" and "Proton Therapy" were used. Articles published from 1 January 2020 in English were included. In total, 138 studies were identified of which 14 articles met the inclusion criteria. A scoping review design was chosen to capture the full extent of information published relating to the aim. Result(s): Six of 14 articles included statements regarding treatment of COVID-19 patients. Three publications recommended deferred or alternative treatment, two indicated to treat urgent/emergency patients and one reported continuous treatment for infectious patients. Recurring impacts on PT provision included more frequent use of alternative therapies, reduced referrals, delayed treatment starts and CT simulation, change in treatment volume and staffing limitations due to pandemic restrictions. Consequently, telehealth consults, remote work, reduction in patient visitors, screening procedures and rigorous cleaning protocols were recommended. Discussion/Conclusion: Few publications detailed patient selection or workflow methods used during the pandemic. Further research is needed to obtain more detailed information regarding current global patient selection methods in proton therapy, collecting this data could aid in future planning for proton therapy in Australia.

Barriers to Engagement in Surgical Simulation Education

Introduction: The COVID-19 pandemic has negatively impacted clinical experience and case volumes. Surgical simulation is now an even more powerful training tool and, to maximize potential, we must ensure learner engagement. Our aim was to identify barriers to surgical simulation engagement and strategies to mitigate these. Method(s): Scoping search was performed with a trained librarian of PubMed, EMBASE and Web of Science. Title and screening were completed with inclusion criteria: articles describing barriers to engagement with surgical simulation. After full text screening, data was extracted from included articles: type of study, MERSQI score, type/number of participants, barriers to engagement and strategies to mitigate these. Result(s): Twenty-nine manuscripts were included with 951 faculty and 2,467 residents. The majority (86%) were in high income countries (HIC) and four in LMICs. Most were surveys (22/29), and five involved semi-structured interviews/focus groups. Mean adjusted MERSQI score was 8. Commonest barriers to HIC engagement were learner clinical duties (9/25), lack of learner time (13/25), lack of learner interest/motivation (9/25) and lack of faculty time or interest to participate (12/25). In LMIC, commonest barriers were lack of simulation lab/equipment (4/4), cost (3/4) and inadequate supervision (3/4). Strategies to improve HIC engagement were mandatory/protected resident simulation training (9/25) and, in LMIC, low cost simulators (4/4) and sharing resources (2/4). Conclusion(s): Identification of barriers to simulation engagement is crucial for successful learning. Given the increased importance of simulation education due to the COVID-19 pandemic, surgical educators should strategize to maximize engagement.

Virtual learning before and during the COVID-19 pandemic: A mixed methods study of Pharmacy students

Background It is a challenge for pharmacy courses worldwide to combine theoretical knowledge with practical skills to equip students for their future practice. Computer-based simulation offers a way of building a bridge between theory and practice. In recent years, digital simulation has expanded rapidly as a new technique of virtual learning. The digital platform ''Pharmacy Simulator'' proposes computer-based encounters with virtual patients to train clinical and communication skills in a community pharmacy setting. However, during the COVID-19 pandemic, while students were digitally resilient and endured the endless challenges of online lectures, many were dealing with Zoom and screen fatigue. Purpose To investigate pharmacy students' acceptance of Pharmacy Simulator before and during a pandemic situation. This focuses on students' self-assessment and confidence in counselling after playing the scenarios on Pharmacy Simulator. Method Two cohorts of Master of Pharmacy students at The University of Western Australia played two scenarios on Pharmacy Simulator in 2019 (anaphylaxis and salbutamol) and 2021 (anaphylaxis and vaccination). A mixed-method analysis was performed with data from (i) qualitative semi-structured interviews carried out in 2019 pertaining to participants' acceptance of Pharmacy Simulator and in 2021 (ii) a questionnaire with 25 items derived from the interviews. The interviews were transcribed verbatim into electronic format with the data management assistance MAXQDA and analyzed inductively using the Framework Method. Questionnaire responses were analyzed in Microsoft Excel using descriptive statistics. Openended questions were evaluated inductively. Findings Data were collected from 20 interviews and 31 answered questionnaires. In 2019, participants reported that Pharmacy Simulator was a fun, engaging, and straightforward learning tool and, therefore, user-friendly. They reported the feedback at the end of the session to be most valuable. The platform was perceived to fill the gap between the theory from lectures and community pharmacy practice. In 2021, participants ''agreed'' (median: 4, on a 5-point Likert scale) with seven statements about Pharmacy Simulator's usability, such as it being a helpful tool for acquiring new knowledge. Participants' confidence in counselling regarding the scenario topics improved. One participant stated, ''It taught me more through trial and error''. Conclusion Pharmacy students reported similar acceptance levels of Pharmacy Simulator before and during the COVID-19 pandemic. The use of simulation during virtual patient encounters seems to facilitate the transfer from theory to practice, independently of learning conditions that were predominantly screen-based.

COVID-19 Model Based on Conformable Fractional Derivativeand Its Numerical Solution

After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model. © 2022 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

Dynamic Safety Assessment and Enhancement of Port Operational Infrastructure Systems during the COVID-19 Era

Seaports function as lifeline systems in maritime transportation, facilitating critical processes like shipping, distribution, and allied cargo handling. These diverse subsystems constitute the Port Infrastructure System (PIS) and have intricate functional interdependencies. The PIS is vulnerable to several external disruptions, and the impact of COVID-19 is severe and unprecedented in this domain. Therefore, this study proposes a novel general port safety framework to cope with recurring hazards and crisis events like COVID-19 and to augment PIS safety through a multi-state failure system. The PIS is divided into three critical subsystems: shipping, terminal, and distribution infrastructure, thereby capturing its functional interdependency and intricacy. A dynamic input-output model is employed, incorporating the spatial variability and average delay of the disruption, to determine the PIS resilience capacity under the stated disruptions. This study simulates three disruption scenarios and determines the functional failure capacity of the system by generating a functional change curve in Simulink. This study offers viable solutions to port managers, terminal operators, and concerned authorities in the efficient running of intricate interdependent processes and in devising efficient risk control measures to enhance overall PIS resilience and reliability. As part of future studies, given the difficulty in obtaining relevant data and the relatively limited validation of the current model, we aim to improve the accuracy and reliability of our model and enhance its practical applicability to real-world situations with data collected from a real-world case study of a PIS system.

IMPACT OF VIRTUAL INTERPROFESSIONAL COVID-19 DISASTER SIMULATION TABLETOP EXERCISE (VICTER) WORKSHOP ON DISASTER PREPAREDNESS AMONG INTERPROFESSIONAL TRAINEES IN A TERTIARY CARE TEACHING HOSPITAL IN INDIA

INTRODUCTION: Disaster planning is of significant importance for the healthcare professional and the healthcare setting. Hospital-based disaster protocols form the cornerstone of disaster response. There is a paucity of data on disaster preparedness training using the virtual tabletop exercise (VTTX) module for interprofessional education from in-hospital and prehospital settings. With the coronavirus disease 2019 (COVID-19) pandemic, we have seen a paradigm shift of education strategies to the virtual realm. Here we attempt to study the impact of an online tabletop exercise workshop on the knowledge and confidence of disaster preparedness among Interprofessional trainees. MATERIAL AND METHODS: Interprofessional trainees from medical, dental, nursing, respiratory therapy, and paramedic domains who consented were included in this study. Institutional ethics committee approval was received and the study was registered with the clinical trials registry India (CTRI), before initiation. The VTTX module has been adapted from the World Health Organization (WHO) COVID-19 training resources. Three international experts from the disaster medicine domain validated the module, questionnaire, and feedback. Wilcoxon signed-rank test was used to compare the parameters (Knowledge and confidence level) pre and post-workshop. RESULTS: A total of 76 candidates with a mean age was 21.67 ± 2.5 (range:19-36) were part of the workshop. Comparison of the median scores and interquartile range of confidence level and knowledge respectively before [38 (29.25-45.75), 9 (7-11)] and after [51.50 (45-60), 11 (10-12)] the workshop showed vital significance (p-value < 0.001). All participants gave positive feedback on the workshop meeting the objectives. The majority agreed that the workshop improved their self-preparedness (90%) and felt that the online platform was appropriate (97.5%) CONCLUSIONS: This study sheds light on the positive impact of the online VTTX based workshop on disaster preparedness training among interprofessional trainees. Disaster preparedness training using available online platforms may be effectively executed with the VICTEr workshop even during the COVID-19 pandemic. The VICTEr workshop serves as a primer for developing online modules for effective pandemic preparedness training in interprofessional education. Copyright © 2023 Via Medica.

Analysis of Maternal Critical Care at the Mid Yorkshire Hospitals NHS Trust

Introduction: Perinatal admissions to Critical Care are increasing due to rising maternal age, obesity, and comorbid disease.1 The MBRRACE Report 2021 stated that of 191 maternal deaths in 2017-2019, only 17% had good care.2 Since the COVID-19 pandemic, there was a subjective increase in perinatal admissions to Mid Yorkshire Hospitals Critical Care. Objective(s): To investigate whether MYH Critical Care maternal admissions have increased, if there has been a change in admission trends and to evaluate the care of critically ill pregnant and postpartum women compared to FICM standards.3 Methods: Retrospective audit of notes of all pregnant and up to 6 weeks postpartum women admitted to critical care between 24/02/2019 and 05/09/2021. Data collected included gestation, duration of admission, organ support, days reviewed by obstetrics and mortality outcomes. Result(s): * There was 1 maternal death and 3 fetal deaths during the study period * 50% of the admissions were antenatal and 50% were postnatal * During the COVID-19 pandemic we have seen a 47% increased rate of admissions from 1 per 29 critical care bed days to 1 per 19 critical care bed days * 50% of patients were supported with ventilation and CPAP during admission, 13% with CPAP only. Prior to the COVID pandemic, no maternal admission required CPAP on our Critical Care unit during the data collection period * 63% of patients were reviewed by obstetrics at least one during their admission, but obstetric review was documented on only 37 of 112 patient days * There is no critical care SOP for perimortem Caesarean section * There is no specialist neonatal resuscitation equipment available on ICU * There is no named ICM consultant responsible for Maternal Critical Care * There is no SOP for support of maternal contact with baby * There is no critical care/obstetric services MDT follow-up Conclusion(s): This study shows that Critical Care admissions have increased, and that care does not follow all the FICM recommendations. Considering this, the following recommendations have been made: * Introduce an SOP and simulation training for peri-mortem section * Introduce neonatal resuscitation equipment box * Nomination of a named ICM Consultant lead for Maternal Critical Care to ensure quality of care and act as liaison * Train critical care staff in supporting contact between a mother and baby, with support from midwifery services * Introduction of Obstetric and Critical Care MDT follow-up.

Immersive Technologies - The future for tracheostomy education?

Introduction: The need for standardised education on tracheostomy care is well recognised.1 Staff frequently report a lack of confidence in caring for those with tracheostomies, as well as the management of adverse events as they occur.2 Over the past decade, healthcare providers have developed strategies to educate staff, however, the covid-19 pandemic has severely hampered the ability to provide this necessary training due to restrictions on access to training rooms, the need for social distancing and the significant clinical demands placed on both trainers and trainees.3 The potential for immersive technologies to augment healthcare training is gaining interest exponentially.4 However, its effectiveness is yet to be clearly understood and as such it is not yet common within healthcare education.5 Based on the above, we aimed to explore the potential of these immersive technologies to overcome the current challenges of tracheostomy education, and to develop future strategies to use immersive technology in healthcare education. Method(s): We received a 400,000 grant from Cardiff Capital Region (CCR) to undertake a rapid innovation project overseen by the SBRI centre of excellence. The project consisted of 3 main phases: 1) feasibility;2) development;and 3) testing. The project was officially launched in April 2021 and lasted 12 months. Project governance was provided via the SBRI for clinical excellence, a project board with representation from Welsh Government, Cardiff University and Cardiff and Vale UHB, and a project team with clinical expertise in both the delivery of tracheostomy education and the provision of simulation training in healthcare. Result(s): Phase 1: During phase one 4 industries were successful and received up to 30,000 to explore the feasibility of immersive technology to support tracheostomy education. The industries were Rescape, TruCorp, Aspire2Be and Nudge Reality. During the feasibility phase all industries focused on the emergency management process utilising existing NHS Wales tracheostomy education resources and the national tracheostomy safety programme. Phase 2: For phase 2, Rescape and Nudge Reality were chosen to develop the technology. These industries continued to work in conjunction with the project team to capture the core elements of tracheostomy care, including multi-user emergency management scenarios. Additional content was also added for bronchoscopy and insertion of intercostal drains. Phase 3: Testing of both solutions was undertaken over an 8-week period, across 6 Health Boards in NHS Wales. The results of the testing will be analysed and available for presentation in due course. Provision findings demonstrate good face and content validity with high levels of user satisfaction. Discussion / Conclusion(s): The provision of essential tracheostomy education has been severely affected by the covid-19 pandemic. Evolving immersive technologies have the potential to overcome these challenges and improve the effectiveness and efficiency of education packages in tracheostomy care and wider. Through this CCR grant, in conjunction with industry, we have developed two solutions with the potential for widescale procurement and future research on the use of immersive technologies within healthcare.