Quantifying the impact of regional variations in COVID-19 infections and hospitalizations across Ireland.

BACKGROUND: As most COVID-19 transmission occurs locally, targeted measures where the likelihood of infection and hospitalization is highest may be a prudent risk management strategy. To date, in the Republic of Ireland, a regional comparison of COVID-19 cases and hospitalizations has not been completed. Here, we investigate (i) the variation in rates of confirmed infection and hospital admissions within geographical units of the Republic of Ireland and (ii) frequency of deviations in risk of infection or risk of hospitalization. METHODS: We analyzed routinely collected, publicly available data available from the National Health Protection and Surveillance Centre and Health Service Executive from nine geographical units, known as Community Health Organization areas. The observational period included 206 14-day periods (1 September 2020-15 April 2021). RESULTS: A total of 206 844 laboratory-confirmed cases and 7721 hospitalizations were reported. The national incidence of confirmed infections was 4508 [95% confidence interval (CI) 4489-4528] per 100 000 people. The risk of hospital admission among confirmed cases was 3.7% (95% CI 3.5-3.9). Across geographical units, the likelihood that rolling 14-day risk of infection or hospitalization exceeded national levels was 9-86% and 0-88%, respectively. In the most affected regions, we estimate this resulted in an excess of 15 180 infections and 1920 hospitalizations. CONCLUSIONS: Responses to future COVID-19 outbreaks should consider the risk and harm of infection posed to people living in specific regions. Given the recent surges of COVID-19 cases in Europe, every effort should be made to strengthen local surveillance and to tailor community-centred measures to control transmission.

Virtual VMAT QA prospective validation: towards measurement-free patient-specific quality assurance

Purpose or Objective The COVID-19 pandemic forced radiation oncology departments to alter clinical workflows to reduce exposure risks in the clinic. Performing patient-specific quality assurance (PSQA) is one of the most resource intensive and time-consuming tasks. With technological advancements in radiotherapy treatment planning and quality assurance, research towards measurement-free PSQA has become a focus within the field. Most of these techniques involve modeling the relationship between treatment plan complexity and corresponding PSQA outcomes. However, to our knowledge, none of these efforts have been assessed and prospectively validated for clinical use. We implemented and a machine learning-based virtual VMAT QA (VQA) workflow to assess the safety and workload reduction of measurement-free patient-specific QA at a multi-site institution in light of COVID-19. Materials and Methods An XGBoost machine learning model was trained and tuned to predict QA outcomes of VMAT plans, represented as percent differences between the planned dose and measured ion chamber point dose in a phantom. The model was developed using a dataset of 579 previous clinical VMAT plans and associated QA measurements from our institution. 30 classes of complexity features were extracted from each VMAT plan and used as input for the model, which was tuned using a grid search over learning rate and tree depth hyperparameters and evaluated with 10-fold cross-validation. The final model was implemented within a webbased VQA application to predict QA outcomes of clinical plans within our existing clinical workflow. The application also displays relevant plan-specific feature importance and nearest neighbor analyses relative to database plans for physicist evaluation and documentation (Figure 1). VQA predictions were prospectively validated over one month of measurements at our clinic to assess the safety and efficiency gains of clinical implementation. $Φg Results 147 VMAT plans were measured at our institution over the course of one month, taking an average of approximately 20 minutes per plan for QA. VQA predictions for these plans had a mean absolute error of 0.97 +/- 0.69%, with a maximum absolute error of 2.75% (Figure 2). Employing a prediction decision threshold of 1% - meaning plans with absolute predictions of less than 1% would not need measurements - would flag all plans that may have ion chamber disagreements greater than 4%. This translates to a 73% reduction in QA workload in terms of time. A more conservative implementation of this workflow, where all SBRT plans will continue to be measured, would still result in a 46% reduction in QA workload. $Φg Conclusion To our knowledge, this is the first prospective clinical implementation and validation of VQA, which we observed to be safe and efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for patient-specific QA, leading to a more effective allocation of clinical resources.

Information systems, people and support structures: A critical realist analysis of a COVID-19 contact tracing centre

Contact tracing is used to identify individuals who have been in close contact with any person who has a presumed or confirmed COVID-19 infection. This paper examines one particular contact tracing centre established in Ireland between March-June 2020. We leverage a critical realist-based philosophical framework and associated methodology to seek the generative mechanisms that determined how this contact tracing centre evolved over its lifetime. Drawing on 14 semi-structured interviews, we hypothesise a total of three mechanisms: the motivation and altruistic nature of superusers and other volunteers;the information systems and communications infrastructure built around the contact tracing centre;and, the training and associated support structures provided to volunteers. Our research suggests that attention should be focussed on developing highly flexible information systems and the identification of superusers as project champions. A significant contribution of this work is providing clear operational guidance for establishing contact tracing centres in Ireland and globally. © AMCIS 2021.

AI in My Life: AI, Ethics & Privacy Workshops for 15-16-Year-Olds

AI in My Life' project will engage 500 Dublin teenagers from disadvantaged backgrounds in a 15-week (20-hour) co-created, interactive workshop series encouraging them to reflect on their experiences in a world shaped by Artificial Intelligence (AI), personal data processing and digital transformation. Students will be empowered to evaluate the ethical and privacy implications of AI in their lives, to protect their digital privacy and to activate STEM careers and university awareness. It extends the ĝ€DCU TY' programme for innovative educational opportunities for Transition Year students from underrepresented communities in higher education. Privacy and cybersecurity researchers and public engagement professionals from the SFI Centres ADAPT1 and Lero2 will join experts from the Future of Privacy Forum3 and the INTEGRITY H20204 project to deliver the programme to the DCU Access5 22-school network. DCU Access has a mission of creating equality of access to third-level education for students from groups currently underrepresented in higher education. Each partner brings proven training activities in AI, ethics and privacy. A novel blending of material into a youth-driven narrative will be the subject of initial co-creation workshops and supported by pilot material delivery by undergraduate DCU Student Ambassadors. Train-The-Trainer workshops and a toolkit for teachers will enable delivery. The material will use a blended approach (in person and online) for delivery during COVID-19. It will also enable wider use of the material developed. An external study of programme effectiveness will report on participants': enhanced understanding of AI and its impact, improved data literacy skills in terms of their understanding of data privacy and security, empowerment to protect privacy, growth in confidence in participating in public discourse about STEM, increased propensity to consider STEM subjects at all levels, and greater capacity of teachers to facilitate STEM interventions. This paper introduces the project, presents more details about co-creation workshops that is a particular step in the proposed methodology and reports some preliminary results. © 2021 Owner/Author.

Safe management of surgical smoke in the age of COVID-19.

BACKGROUND: The COVID-19 global pandemic has resulted in a plethora of guidance and opinion from surgical societies. A controversial area concerns the safety of surgically created smoke and the perceived potential higher risk in laparoscopic surgery. METHODS: The limited published evidence was analysed in combination with expert opinion. A review was undertaken of the novel coronavirus with regards to its hazards within surgical smoke and the procedures that could mitigate the potential risks to healthcare staff. RESULTS: Using existing knowledge of surgical smoke, a theoretical risk of virus transmission exists. Best practice should consider the operating room set-up, patient movement and operating theatre equipment when producing a COVID-19 operating protocol. The choice of energy device can affect the smoke produced, and surgeons should manage the pneumoperitoneum meticulously during laparoscopic surgery. Devices to remove surgical smoke, including extractors, filters and non-filter devices, are discussed in detail. CONCLUSION: There is not enough evidence to quantify the risks of COVID-19 transmission in surgical smoke. However, steps can be undertaken to manage the potential hazards. The advantages of minimally invasive surgery may not need to be sacrificed in the current crisis.