Covid severity prediction: Who cares about the data quality?

COVID-19 is an ongoing pandemic disrupting daily life and overwhelming the healthcare infrastructure. Since the outburst of the pandemic, researchers have used various techniques to predict many aspects of the disease, including mortality rate and severity. The reproducibility of this research is challenging due to varying methodologies used to collect data, data quality, vague description of methodological approach to training prediction models, over-relying on data imputation, and over-fitting. This paper focuses on these challenges and provides a short yet comprehensive review of research on COVID mortality and severity prediction. The emphasis is on the reproducibility of the results and data quality issues. To further elaborate on the issue, we report the development of severity prediction models using two data sets. CRISP-DM is used as a methodological approach. We analyze and criticize the quality of the used data sets and how they affect the performance and limitations of the trained models. We conclude this paper with comments on data quality issues, the importance of reproducibility, and suggestions to improve reproducibility. © 2022 IEEE.

A methodological approach for modeling the spread of disease using geographical discrete-event spatial models

The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available. © The Author(s) 2023.

Implementation of DMAIC Cycle to Study the Impact of COVID-19 on Emergency Department-LOS

Lean Six Sigma (LSS) is a methodological approach that originated in industry and has, over time, become increasingly popular in healthcare. Its tool-to, the DMAIC cycle, consisting of 5 main steps, offers methodological rigor that helps improve processes by comparing results quantitatively. In this study, the LSS and in particular the DMAIC cycle was used to investigate the impact of COVID-19 on patients' length of stay in the Emergency Department (ED-LOS) of the Evangelical Hospital "Betania” of Naples (Italy). The study revealed a general increase in ED-LOS due mainly to the new steps that the hospital added to the standard flow, such as those for performing screening swabs, and the reduction of treatment stations, with the exception of patients discharged home for whom there was a statistically significant reduction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Main Threats to the Cybersecurity Management System in the Context of COVID 19

The main purpose of the article is to form a methodological approach to countering the major threats to the cybersecurity management system in the context of COVID-19. Cybersecurity is a complex conception and requires a careful approach. Today, in conditions when the pandemic has changed all the rules of doing business and requires alternative approaches from the organization management system, talking about the security of the digital space, which is actively used during a pandemic, is very relevant. We have applied methods of expert analysis with the involvement of specialists in the field of cybersecurity and methods of hierarchical ordering of certain threats. Because of our research in the article, an approach was proposed to counter the major threats to the cybersecurity management system in the context of COVID 19. The article is intended to form an approach to countering threats to cybersecurity management systems in organizations. Further research should actively try to apply the proposed approach in practice. © 2021 IEEE.

Reducing statistics anxiety for psychology students during the global pandemic: a methodology approach

Statistics anxiety is a significant problem for students’ learning experience in the psychological sciences. This article explores a novel teaching methodological approach during the global pandemic to improve students’ performance by reducing their statistics anxiety. The subject design and teaching methodological approach were developed based on twenty years of experience taking into consideration the student feedback, while its implementation was aligned with the 5-E’s Victorian Teaching and Learning pedagogical Model (VTLM). Moreover, to reduce the statistics anxiety, two sections were embedded in the Learning Management System of the subject: (1) an interactive web-based mindfulness intervention and (2) a head-start ‘Ready, Set, Go’ information pack. The implementation of these approaches resulted in a better understanding of statistical concepts and utilization of the SPSS software by the majority of the students (80% pass-rate). Consequently, a 6% increase in pass rates and an average of 32% increase in satisfaction scores for each item in the university central survey were observed. While the uniqueness of the first year of COVID-period makes comparisons difficult, it is matched by the urgency of the need for effective interventions to support student success. [ FROM AUTHOR] Copyright of International Journal of Mathematical Education in Science & Technology 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.)

Cell lysis analysis for respiratory viruses through simulation modeling

An ordinary system of differential equations leading to a simulation model is propose as methodological approach to analysis the incidence of infectious-contagious diseases, in this case using SARS-CoV-2 virus as pathogenic model. The dynamics of the model are drive by the interaction between susceptible cells contemplating respiratory epithelial cells and viral infection mediated by two types of lysis response. To perform the simulations, values of some variables and parameters were selected from referenced sources, considering that previous reports suggested that the viral load in the lower respiratory tract might reach its peak in the second week after the beginning of disease symptoms. The scenarios described in the simulations evidence the performance of the cell lysis response from susceptible cells that have been infected. The recommend model shows that an excess response from both the original virus and the mutated virus leads to an increase in the approximate time to control viral infection within the organism. © 2022 Institute of Physics Publishing. All rights reserved.

DESIGN INNOVATION of BICYCLE FRAMES EXPLOITING TOPOLOGY OPTIMIZATION

In recent years, the use and demand of bicycles have increased thanks to the growing attention to the environment and due to the COVID-19 pandemic situation. At the same time, the design of the bicycles has remained substantially unchanged, improving in materials and components technology. In the off-road sector, two diametrically opposed categories have emerged in terms of comfort and pedalling efficiency. The goal of this research is to introduce a first methodological approach for the optimization of a mountain bike frame. The behaviour of the developed frame aims to combine the pedalling benefits now available only in different and non-comparable bicycle configurations. The first step concerns the modelling of a generic off-road bicycle frame, then its behaviour has been simulated for specific load cases. Subsequently, the part of the bicycle that best performed the double function of compliance and rigidity has been sought through an analysis of the strain energy using FE simulations. Hence, the reference region has been topologically optimized to provide adequate chassis travel performance. The analysis scheme has been iteratively repeated also on other parts of the frame until an acceptable solution is obtained for the utilize presented. The final configuration permits a rear tube and bottom bracket displacement of 10.4 mm and 2.4 mm compared to the 0.5 mm and 0.4 mm of the original frame respectively. The approach described can be proposed as a support for the search for an innovative design for products with unchanged geometries due to the inertia of the designers. At the same time, this methodology aims to expand the possible use of topological optimization, moving away from the classic constraint schemes present in various software. © 2021 American Society of Mechanical Engineers (ASME). All rights reserved.

Latinx Community College Students and the (In)Opportunities Brought by COVID-19 Pandemic

The disruption to higher education institutions across the United States created by COVID-19 affected more than 20 million college students. States cancelled in-person classes and campus activities quickly shifted to remote and virtual learning. The pandemic, along with its economic impact, altered education for community college students, creating a traumatic event that exasperated mental health conditions. In addition, job loss increased food insecurities drastically during COVID-19 and students across the country turned to their colleges for wraparound services. In this proposal, the authors review the impact of COVID-19 on marginalized college students, specifically Latinx students in a South Texas college. The findings revealed that Latinx students navigated virtual learning differently and were impacted by their access to technology, as well as their access to mental health services and food pantries. [ FROM AUTHOR] Copyright of Journal of Latinos & Education 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.)

Based-on simulation training on ventilation calculation for the reduction of occupational risk of SARS-CoV-2 infection

This paper addresses a new learning methodological approach compatible with e-learning and m-learning for undergraduate engineering students to learn the ventilation requirements for the prevention of SARS-CoV-2 virus infection in the workplace and to develop the critical sense to design ventilation systems in accordance with the standards that minimize the biological risk. Thus, a learning activity based on a simulation of infection risks is proposed in which three different initial ventilation requirements hypotheses were considered to calculate the necessary ventilation in the workplace. Subsequently, a simulation was carried out to obtain the probability of contagion in each of them and, in this way, to obtain information to make adequate decisions and improve critical thinking in the context of industrial hygiene. © 2021 ACM.

Population Vulnerability to the SARS-CoV-2 Virus Infection. A County-Level Geographical-Methodological Approach in Romania.

The assessment and identification of risk/vulnerable groups and risk factors are vital elements that can help quantify the pandemic potential of the SARS-CoV-2 virus in order to plan prevention and treatment measures. The aim of the study is to identify a methodological approach of population vulnerability to the SARS-CoV-2 virus infection. The study identifies reliable data sources and sets up a unitary database with statistical variables, quantitative and qualitative indicators with potential for being updated and improved. The analysis takes into account a number of variables/indicators (e.g., elderly persons, population without physician care, number of people suffering from cardiovascular diseases, number of people suffering from respiratory diseases, dwellings not connected to the public water supply network, no. of medical staff, number of COVID-19 hospitals, PCR testing laboratories, number of vaccinated persons) grouped into the key vulnerability components: exposure, sensitivity, coping capacity and adaptive capacity. They allowed the computation of the final Index of Population Vulnerability to the SARS-CoV-2 virus infection and the mapping of different dimensions of vulnerability. The study was performed using the statistical data available at NUTS3/County level provided by different institutions (e.g., the Ministry of Health, the National Institute of Public Health, the Strategic Communication Group, and the National Institute of Statistics). The mapping of the different degrees of vulnerability could solve a problem of visibility for possible areas with vulnerable population, but also a problem of communication between different institutional health and administrative levels, as well as between all of them and the local communities and/or professionals.

Fast, Scalable, and Practical: An Alkaline DNA Extraction Pipeline for Emergency Microbiomics Biosurveillance.

Pandemics and environmental crises evident from the first two decades of the 21st century call for methods innovation in biosurveillance and early detection of risk signals in planetary ecosystems. In crises conditions, conventional methods in public health, biosecurity, and environmental surveillance do not work well. In addition, the standard laboratory amenities and procedures may become unavailable, irrelevant, or simply not feasible, for example, owing to disruptions in logistics and process supply chains. The COVID-19 pandemic has been a wakeup call in this sense to reintroduce point-of-need diagnostics with an eye to limited resource settings and biosurveillance solutions. We report here a methodology innovation, a fast, scalable, and alkaline DNA extraction pipeline for emergency microbiomics biosurveillance. We believe that the presented methodology is well poised for effective, resilient, and anticipatory responses to future pandemics and ecological crises while contributing to microbiome science and point-of-need diagnostics in nonelective emergency contexts. The alkaline DNA extraction pipeline can usefully expand the throughput in emergencies by deployment or to allow backup in case of instrumentation failure in vital facilities. The need for distributed public health genomics surveillance is increasingly evident in the 21st century. This study makes a contribution to these ends broadly, and for future pandemic preparedness in particular. We call for innovation in biosurveillance methods that remain important existentially on a planet under pressure from unchecked human growth and breach of the boundaries between human and nonhuman animal habitats.