COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

New ITU-T Study Group 5 Structure and Deliberations

The NTT Group is participating in the international standardization activities in the International Telecommunication Union - Telecommunication Standardization Sector (ITU-T) Study Group (SG) 5 to protect telecommunication facilities from electromagnetic interference and lightning surges, assess the impact of information and communication technologies on climate change, address the issue of a circular economy that enables sustainable development, and contribute to improving the reliability of telecommunication services and reducing the environmental impact of its business activities. In this article, we introduce the study structure of ITU-T SG5 for the new study period (2022–2024), which has finally started due to the COVID-19 pandemic, as well as the latest discussion trends at the first meeting held in June 2022. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

Detecting and Classifying Misplaced Catheters III Chest X-Rays Based on Efficient net B7

Catheter tip misalignment can lead to complications in patients together with serious medical malpractice cases. This article aims at the current surge in COVID-19 patients. Using X-ray imaging datasets from COVID-19 patients, previously published on Kaggle as 'RANZCR CLiP - Catheter and Line Position Challenge' and hosted by the Royal Australian and NZ College of Radiologists, a deep-learning algorithm was utilized to detect the position of the patient's catheter and automatically determine whether the catheter tip is misplaced or otherwise. This study employed U-Net to segment and identify catheter position types, together with employing Efficiency net B7 to determine whether the misaligned catheter is misaligned which scores 0.959(AUC). In addition, results were also compared using Efficiency Net B5, ResNet 200D. © 2022 IEEE.

GEO-SPATIAL BIG DATA ANALYSIS of COVID-19 for SOUTHEAST ASIAN COUNTRIES SITUATED at LATITUTE below 38°N

The outbreak of the Covid-19 originated from Wuhan City, quickly spread across China and beyond following human mobility patterns covering more than 210 countries of the globe, and World Health Organization (WHO) declared the outbreak a pandemic on 11 March 2020. The outbreak of Covid-19 spread geo-spatial and spatiotemporal way in countries situated at latitude between 64°N and 35°S, causing more than 182.969 million (182,969,081) people of the global population infected and 3.963 million (3,963,102) deaths ( as on 30 June 2021). The spatial spreading of covid-19 spectrum due to large-scale migrations were reported in the southeast Asian region, with the first case in Thailand on 13 January 2020, which is followed by South Korea on 20 January 2020, and Vietnam and Taiwan on 22 January 2020 prior to reach Hong Kong and Singapore on 23 January 2020. Malaysia reported the first covid-19 case on 25 January 2020, which further spread to Philippines on 30 January 2020 prior to reach Indian Sub-continent on 31 January 2020. There are marked variations in the spectrum of daily new Covid-19 cases and population mortality between different countries in the Southeast Asian region such as India, South Korea, Taiwan, Vietnam, Singapore, Thailand, Malaysia, Indonesia, Philippines, Hong-Kong, and Myanmar. In this paper, spatial big data predictive analysis have been carried out based 5-days moving averages of new covid-19 cases from 19 February 2020 to 30 June 2021, which shows multiple surge of covid-19 spectrum in the southeast Asian region. This paper further describes the impact of latitude on population mortality for determining the severity of the outbreak based on population mortality data of 28 countries situated at latitude below 64°N from 15 April 2020 to 20 January 2021, whereas relatively lower population mortality observed for the countries situated at latitude below 38°N. © ACRS 2021.All right reserved.

Impact of Coastal Disasters on Women in Urban Slums: A New Index

Coastal hazards, particularly cyclones, floods, erosion and storm surges, are emerging as a cause for major concern in the coastal regions of Vijayawada, Andhra Pradesh, India. Serious coastal disaster events have become more common in recent decades, triggering substantial destruction to the low-lying coastal areas and a high death toll. Further, women living in informal and slum housing along the Vijayawada coastline of Andhra Pradesh (CAP), India, suffer from multiple social, cultural and economic inequalities as well. These conditions accelerate and worsen women’s vulnerability among this coastal population. The existing literature demonstrates these communities’ susceptibility to diverse coastal disasters but fails to offer gender-specific vulnerability in urban informal housing in the Vijayawada area. Accordingly, the current study developed a novel gender-specific Women’s Coastal Vulnerability Index (WCVI) to assess the impact of coastal disasters on women and their preparedness in Vijayawada. Field data was collected from over 300 women through surveys (2) and workshops (2) between November 2018 and June 2019, and Arc-GIS tools were used to generate vulnerability maps. Results show that women are more vulnerable than men, with a higher death rate during coastal disaster strikes. The current study also found that gender-specific traditional wear is one of the main factors for this specific vulnerability in this area. Furthermore, the majority of the women tend to be located at home to care for the elders and children, and this is associated with more fatalities during disaster events. Homes, particularly for the urban poor, are typically very small and located in narrow and restricted sites, which are a barrier for women to escape from unsafe residential areas during disasters. Overall, the research reveals that most of the coastal disaster events had a disproportionately negative impact on women. The results from this present study offer valuable information to aid evidence-based policy- and decision-makers to improve existing or generate innovative policies to save women’s lives and improve their livelihood in coastal areas.

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Since the emergence of coronavirus disease–2019 (COVID-19) outbreak, every country has implemented digital solutions in the form of mobile applications, web-based frameworks, and/or integrated platforms in which huge amounts of personal data are collected for various purposes (e.g., contact tracing, suspect search, and quarantine monitoring). These systems not only collect basic data about individuals but, in most cases, very sensitive data like their movements, spatio-temporal activities, travel history, visits to churches/clubs, purchases, and social interactions. While collection and utilization of person-specific data in different contexts is essential to limiting the spread of COVID-19, it increases the chances of privacy breaches and personal data misuse. Recently, many privacy protection techniques (PPTs) have been proposed based on the person-specific data included in different data types (e.g., tables, graphs, matrixes, barcodes, and geospatial data), and epidemic containment strategies (ECSs) (contact tracing, quarantine monitoring, symptom reports, etc.) in order to minimize privacy breaches and to permit only the intended uses of such personal data. In this paper, we present an extensive review of the PPTs that have been recently proposed to address the diverse privacy requirements/concerns stemming from the COVID-19 pandemic. We describe the heterogeneous types of data collected to control this pandemic, and the corresponding PPTs, as well as the paradigm shifts in personal data handling brought on by this pandemic. We systemically map the recently proposed PPTs into various ECSs and data lifecycle phases, and present an in-depth review of existing PPTs and evaluation metrics employed for analysis of their suitability. We describe various PPTs developed during the COVID-19 period that leverage emerging technologies, such as federated learning, blockchain, privacy by design, and swarm learning, to name a few. Furthermore, we discuss the challenges of preserving individual privacy during a pandemic, the role of privacy regulations/laws, and promising future research directions. With this article, our aim is to highlight the recent PPTs that have been specifically proposed for the COVID-19 arena, and point out research gaps for future developments in this regard. Author

Differences in mortality in critically ill elderly patients during the second COVID-19 surge in Europe.

BACKGROUND: The primary aim of this study was to assess the outcome of elderly intensive care unit (ICU) patients treated during the spring and autumn COVID-19 surges in Europe. METHODS: This was a prospective European observational study (the COVIP study) in ICU patients aged 70 years and older admitted with COVID-19 disease from March to December 2020 to 159 ICUs in 14 European countries. An electronic database was used to register a number of parameters including: SOFA score, Clinical Frailty Scale, co-morbidities, usual ICU procedures and survival at 90 days. The study was registered at ClinicalTrials.gov (NCT04321265). RESULTS: In total, 2625 patients were included, 1327 from the first and 1298 from the second surge. Median age was 74 and 75 years in surge 1 and 2, respectively. SOFA score was higher in the first surge (median 6 versus 5, p < 0.0001). The PaO2/FiO2 ratio at admission was higher during surge 1, and more patients received invasive mechanical ventilation (78% versus 68%, p < 0.0001). During the first 15 days of treatment, survival was similar during the first and the second surge. Survival was lower in the second surge after day 15 and differed after 30 days (57% vs 50%) as well as after 90 days (51% vs 40%). CONCLUSION: An unexpected, but significant, decrease in 30-day and 90-day survival was observed during the second surge in our cohort of elderly ICU patients. The reason for this is unclear. Our main concern is whether the widespread changes in practice and treatment of COVID-19 between the two surges have contributed to this increased mortality in elderly patients. Further studies are urgently warranted to provide more evidence for current practice in elderly patients. TRIAL REGISTRATION NUMBER: NCT04321265 , registered March 19th, 2020.

Not all COVID-19 pandemic waves are alike.

OBJECTIVE: We aimed to assess differences in patients' profiles in the first two surges of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Barcelona, Spain. METHODS: We prospectively collected data from all adult patients with SARS-CoV-2 infection diagnosed at the Hospital de la Santa Creu i Sant Pau, Barcelona, Spain. All the patients were diagnosed through nasopharyngeal swab PCR. The first surge spanned from 1st March to 13th August 2020, while surge two spanned from 14th August to 8th December 2020. RESULTS: There were 2479 and 852 patients with microbiologically proven SARS-CoV-2 infection in surges one and two, respectively. Patients from surge two were significantly younger (median age 52 (IQR 35) versus 59 (40) years, respectively, p < 0.001), had fewer comorbidities (379/852, 44.5% versus 1237/2479, 49.9%, p 0.007), and there was a shorter interval between onset of symptoms and diagnosis (median 3 (5) versus 4 (5) days, p < 0.001). All-cause in-hospital mortality significantly decreased for both the whole population (24/852, 2.8% versus 218/2479, 8.8%, p < 0.001) and hospitalized patients (20/302, 6.6% versus 206/1570, 13.1%, p 0.012). At adjusted logistic regression analysis, predictors of in-hospital mortality were older age (per year, adjusted odds ratio (aOR) 1.079, 95%CI 1.063-1.094), male sex (aOR 1.476, 95%CI 1.079-2.018), having comorbidities (aOR 1.414, 95%CI 0.934-2.141), ICU admission (aOR 3.812, 95%CI 1.875-7.751), mechanical ventilation (aOR 2.076, 95%CI 0.968-4.454), and coronavirus disease 2019 (COVID-19) during surge one (with respect to surge two) (aOR 2.176, 95%CI 1.286-3.680). CONCLUSIONS: First-wave SARS-CoV-2-infected patients had a more than two-fold higher in-hospital mortality than second-wave patients. The causes are likely multifactorial.

Emergence, consolidation and dominance of meta-regimes: Exploring the historical evolution of mass production (1765-1972) from the Deep Transitions perspective.

Grand environmental and societal challenges have drawn increasing attention to system innovation and socio-technical transitions. A recent Deep Transitions framework has provided a comprehensive theory of the co-evolutionary patterns of multiple socio-technical systems over the last 250 years. However, so far the framework has not been subjected to systematic empirical exploration. In this paper we address this gap by exploring the co-evolutionary model linking niche-level dynamics, transitions in single systems and 'great surges of development', as conceptualized by Schot and Kanger (2018) [1]. For this purpose, we conduct a case study on the historical evolution of mass production in the Transatlantic region from 1765 to 1972. Instead of focusing on dominant technologies or common practices the development of mass production is understood as the emergence of a meta-regime, i.e. a set of mutually aligned rules guiding production activities in multiple socio-technical systems. The results broadly confirm the overall model but also enable to extend the Deep Transitions framework by uncovering new mechanisms and patterns in the variation, diffusion and contestation of meta-regimes.