Dynamics of Change at Work and Reactions of Coworking Spaces in the Aftermath of the Pandemic: Notes on Portugal

New workspaces, such as coworking spaces (CSs), have had exponential growth in Portugal in recent years, both in number and variety. Lockdowns and other restrictive measures on mobility, employment and business activity during the COVID-19 pandemic have significantly impacted all sectors, albeit differently. This chapter summarises the central events related to the pandemic crisis in Portugal, reflecting on the impacts observed and highlighting the main legislative measures adopted by the government and the subsequent increasing expressiveness of telework. Therefore, in addition to the gradual transformation of work, CSs have gained a growing space in the media and on the political agenda. Based on ongoing research, this article aims to provide a brief overview of the pandemic effects on Portuguese coworking reality and related issues, pointing out insights for the future. © 2023, The Author(s).

Assessing the Benefits of a Teleassessment Solution Using a FVM Perspective

The recent COVID-19 pandemic has served to highlight the benefits of digital health in general and telehealth in particular. One area of telehealth that is particularly important is that of teleassessment. Currently, we are witnessing an exponential growth in total knee and total hip replacements (TKR) (THR) due to an aging population coupled with longer life expectancy which is leading to a high likelihood of an unsustainable burden for healthcare delivery in Australia. To address this imminent challenge, the following proffers a tele-assessment solution, ARIADNE (Assist foR hIp AnD kNEe), that can provide high quality care, with access for all and support for high value outcomes. A fit viability assessment is provided to demonstrate benefits of the proffered solution. © 2022 IEEE Computer Society. All rights reserved.

Using parameters of piecewise approximation by exponents for epidemiological time series data analysis

Nowadays, detailed epidemiological data are available in the form of time series data. Theoretically, those data can be adequately described by different dynamic models containing exponential growth and exponential decay elements. Practically, parameters of those models are not constants - they can change in time because of many factors like changing hygiene policies, changing social behavior and vaccination. Hence, it was decided to use a piecewise approach: short sequential fragments of time series data are approximated by a function containing some parameters. Analysis of synthetic and real-life Coronavirus disease 2019 data demonstrates that the proposed approach can be used to evaluate the validity of mathematical epidemiological models under test for the different periods of time. More real-life data from different countries must be analyzed in order to recommend an optimal set of the smoothing parameters, and to evaluate the reliability of the proposed approach for the analysis of real-life data. © ISTE Ltd 2022.

Tradeoff between speed and reproductive number in pathogen evolution

The rapid succession of new variants of SARS-CoV-2 emphasizes the need to understand the factors driving pathogen evolution. Here, we investigate a possible tradeoff between the rate of progression of a disease and its reproductive number. Using an SEIR framework, we show that in the exponential growth phase of an epidemic, there is an optimal disease duration that balances the advantage of a fast disease progression with that of causing many secondary infections. This result offers one possible explanation for the ever shorter generation times of novel variants of SARS-CoV-2, as it progressed from the original strain to the Alpha, Delta, and, from late 2021 onwards, to several Omicron variant subtypes. In the endemic state, the optimum disappears and longer disease duration becomes advantageous for the pathogen. However, selection pressures depend on context: mitigation strategies such as quarantine of infected individuals may slow down the evolution towards longer-lasting, more infectious variants. This work then suggests that, in the future, the trend towards shorter generation times may reverse, and SARS-CoV-2 may instead evolve towards longer-lasting variants. © 2023 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Impact of Sudden Change in Mode of Learning and Proposed Teaching Methods on Students

One of the impacts of COVID-19 is the exponential growth of online classroom teaching. However, grasping the reaction and understanding of the students in an online classroom is difficult for the teachers. This is especially a problem in Project Based Learning (PBL) courses with practical exercises. This study provides a "place” to share information about problems encountered by each student. Other students can then use this information to perform practical exercises remotely. As a result, we were able to identify bottlenecks in the exercise tasks by creating "empathize with problems” among students. © 2023 The Institute of Electrical Engineers of Japan.

An Application of the Disease Ontology (DO) for Clustering COVID-19 Hospitalizations in Rio de Janeiro

On the 21st century, the exponential growth of technology, led the world facing a myriad of information coming from multitudinous sources. Then, finding ways of storing knowledge committed to certain rules became imperious. Ontologies have been playing an important role on connecting data to the semantics of the real world. Data, without such ontological commitment, could be interpreted as representations of different entities than the one it actually is, leading to biased analysis and inaccurate prediction on data-driven projects. Such kind of artifact formalizes shared knowledge regarding a domain of discourse. Therefore, this study will, based on works showing the benefits of bringing ontologies to the scenario of Machine Learning techniques, enrich similarity metrics between instances of data. So, the Human Disease Ontology (DO) will be used. Instead of calculating pairwise similarities between two diseases (terms on DO), groups of diseases will be considered. Therefore, this work will rely on adapting a groupwise similarity metric Data collection will be done considering the SIVEP-Gripe Dataset. Then, an analysis will be made on how better Machine Learning Algorithms can perform the analysis is made considering semantic rather than just numerical and categorical features. © 2022 Copyright for this paper by its authors.

Know better, do better: The role of exponential growth understanding in decision making during the COVID-19 pandemic

People mistakenly use the term "exponential growth" to depict a fast-growing process rather than a specific mathematical concept with implications for the spread of the COVID-19 pandemic. Policies promulgated by the authorities during this period were misunderstood and resulted, in many cases, with shocking results worldwide. Biases associated with lack of complete understanding of the speed that the virus was spreading had an impact on the decision-making process. In particular, policy makers had to determine the proper balance between life-saving guidelines and economic costs associated with containment measures. In the future, governments must learn to manage such situations by better appreciating the impact of exponential growth to respond properly when a pandemic may reoccur. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Emphasizing visual tools: using R to understand dynamic models of biology and epidemiology

Epidemiological models have enhanced relevance because of the COVID-19 pandemic. In this note, we emphasize visual tools that can be part of a learning module geared to teaching the SIR epidemiological model, suitable for advanced undergraduates or beginning graduate students in disciplines where the level of prior mathematical knowledge of students may not be very strong. Visual tools – phase portrait, flow field and trajectory and line plots – available in the R software are presented in a step by step manner, moving from the exponential growth model to the logistic growth model and then to the SIR model. Code for numerical simulation of differential equations and estimation of parameters is presented for the SIR model. Suggestions for students to connect the learning from these examples with research papers on COVID-19 are provided.

Overview of Mexican Legal Education

Mexico is the country with the largest number of law schools in the world. In the 2019-2020 academic year, 1954 Institutions of Higher Education had at least one active law degree program. In recent decades, various events have directly impact on the constant and sustained growth of these programs. This article aims to chronologically analyze and describe the events that have influenced the consolidation of the legal education model in Mexico, in the high number of law schools and in the most recent changes of their contents and modalities derived from structural legal reforms and the covid-19 pandemic. © 2022 Authors. All rights reserved.

SIR-M Epidemic Model: A SARS-CoV-2 Perspective

The outbreak of SARS-CoV-2 in November 2019 has been modeled into the Susceptible-Infectious-Recovery (SIR) model initially. The second wave outbreak of the mutant SARS-CoV-2 has statistically proved to hold an exponential growth of mortality rate, especially in India. The infection-recovery strategy observed could be easily modeled to the SIR(Susceptible-Infectious-Recovery) model. Also, the studies included the exposure to the virus resulting in SIER (Susceptible-Infection-Exposure-Recovery) model. The daily statistics published by the World Health Organization (WHO) also reveal unavoidable statistics that represent the count of deaths. As of May 30, 2021, India has reported 27 million total infected cases, and 32 hundred thousand deaths have been reported. This alarms that the spread of pandemics cannot be visualized as a simple SIR model. The significant ratio between the infected and the mortals leads to remodeling the SIR model as SIR-M(Susceptible-Infectious-Recovery-Mortality) model. This paper includes the death count into the model and remodels the SIR model as SIR-M(Susceptible-Infectious-Recovery-Mortality) model. Our proposed model includes a factor, namely δ, which primarily depends on the medical condition. This δ is the primary cause of the deaths in the SARS-Cov-2-affected patients. We have studied the causes of mortality in COVID-19 patients and have validated our model with the real-time COVID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analyzing the Need for Video Summarization for Online Classes Conducted During Covid-19 Lockdown

The evolution of wearable cameras has revolutionized the exponential growth of the industry due to the production of video content. This has ultimately generated the need for storage management, video management, video summarization, methods to reduce the cost of resources, etc. The present paper compares and evaluates the different techniques available for video summarization nowadays and to visualize the need of applying these methods to the content of online classes delivered during the COVID-19 pandemic. This pandemic has affected the lives of children as they all have got stuck totally onto the screen for their education and learning through their respective organizations. But this trend has raised many challenges among the parents, educational lists, and health experts. These challenges mainly include the availability of online gadgets with internet connectivity, especially in rural areas, and the health issues generated because of the overexposure to online gadgets by students all over the world. As per the educational experts, including UNESCO and UNICEF, this continuous exposure to online gadgets has alarmed the different potential dangers to their health including obesity, stubbornness, heart diseases, vision loss, etc. This paper mainly focuses on the need for summarizing the online class videos not only to reduce the burden of the overhead cost of Internet and resources, but also to reduce the harmful effects produced on the health of these students due to the continuous exposure of the unnatural light and waves generated through these gadgets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

SARS-CoV-2 infections in 165 countries over time.

BACKGROUND: Understanding the dynamics of the COVID-19 pandemic and evaluating the efficacy of control measures requires knowledge of the number of infections over time. This number, however, often differs from the number of confirmed cases because of a large fraction of asymptomatic infections and different testing strategies. METHODS: This study uses death count statistics, age-dependent infection fatality risks, and stochastic modeling to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections among adults (aged 20 years or older) in 165 countries over time, from early 2020 until June 25, 2021. The accuracy of the approach is confirmed through comparison with previous nationwide seroprevalence surveys. RESULTS: The estimates presented reveal that the fraction of infections that are detected vary widely over time and between countries, and hence confirmed cases alone often yield a false picture of the pandemic. As of June 25, 2021, the nationwide cumulative fraction of SARS-CoV-2 infections (cumulative infections relative to population size) was estimated as 98% (95% confidence interval [CI] 93-100%) for Peru, 83% (95% CI 61-94%) for Brazil, and 36% (95% CI 23-61%) for the United States. CONCLUSIONS: The time-resolved estimates presented expand the possibilities to study the factors that influenced and still influence the pandemic's progression in 165 countries.

Know better, do better: The role of exponential growth understanding in decision making during the COVID-19 pandemic

People mistakenly use the term "exponential growth" to depict a fast-growing process rather than a specific mathematical concept with implications for the spread of the COVID-19 pandemic. Policies promulgated by the authorities during this period were misunderstood and resulted, in many cases, with shocking results worldwide. Biases associated with lack of complete understanding of the speed that the virus was spreading had an impact on the decision-making process. In particular, policy makers had to determine the proper balance between life-saving guidelines and economic costs associated with containment measures. In the future, governments must learn to manage such situations by better appreciating the impact of exponential growth to respond properly when a pandemic may reoccur. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Does COVID-19 Behave as Lightning Strike?

Early stages of an epidemic are characterized by exponential growth in the number of infected cases, corresponding to the effective reproduction number greater than 1. After deliberate interventions in the disease transmission are introduced, the effective reproduction number should drop below 1. The number of active infections should follow the downward trend conditioned by the stringency of the measures and drop exponentially. The growth phase is in general of shorter duration than the decay phase. This asymmetry imposes itself as an aggravating factor onto common mathematical models used to capture the epidemic dynamics. To overcome aforementioned issue, in this paper, we compare the functional form of the epidemic dynamics with the analytical expression often found in the lightning research and standardization. Computational examples are given for different countries that kept track of the number of daily positive cases, recovered cases and deaths during the period of the first outbreak of Coronavirus disease 2019 (COVID-19). © 2022 University of Split, FESB.

Technology in a fight against COVID-19

The first case of COVID-19 was found in December 2019 which was followed by an exponential growth in the of people infected worldwide. On March 11, 2020 world Heath organisation declared the outbreak of coronavirus a global pandemic and this situation continues till date, i.e. May 2021. The scarcity of resources and knowledge about treating COVID-19 and with an increasing number of infected persons and deaths has made government bodies worldwide struggle to save the lives of people as well as to keep healthcare professionals safe from the virus. For almost a year, people have had to adapt to the new normal and follow some safety guidelines to keep themselves from getting infected. In such difficult times, technological advancements have played a vital role, be it diagnosis, treatment, contact tracing containment of the spread or even helping people with the new normal lifestyle. In this paper, we have discussed the type of technologies that have emerged to fight against COVID-19. We have also discussed the strengths, weaknesses opportunities and threats with the help of SWOT analysis of the technology used in various fields mentioned. © 2022 IEEE.

SIR-M Epidemic Model: A SARS-CoV-2 Perspective

The outbreak of SARS-CoV-2 in November 2019 has been modeled into the Susceptible-Infectious-Recovery (SIR) model initially. The second wave outbreak of the mutant SARS-CoV-2 has statistically proved to hold an exponential growth of mortality rate, especially in India. The infection-recovery strategy observed could be easily modeled to the SIR(Susceptible-Infectious-Recovery) model. Also, the studies included the exposure to the virus resulting in SIER (Susceptible-Infection-Exposure-Recovery) model. The daily statistics published by the World Health Organization (WHO) also reveal unavoidable statistics that represent the count of deaths. As of May 30, 2021, India has reported 27 million total infected cases, and 32 hundred thousand deaths have been reported. This alarms that the spread of pandemics cannot be visualized as a simple SIR model. The significant ratio between the infected and the mortals leads to remodeling the SIR model as SIR-M(Susceptible-Infectious-Recovery-Mortality) model. This paper includes the death count into the model and remodels the SIR model as SIR-M(Susceptible-Infectious-Recovery-Mortality) model. Our proposed model includes a factor, namely δ, which primarily depends on the medical condition. This δ is the primary cause of the deaths in the SARS-Cov-2-affected patients. We have studied the causes of mortality in COVID-19 patients and have validated our model with the real-time COVID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Doubling time of infectious diseases.

The concept of doubling time has been increasingly used since the onset of the coronavirus disease 2019 (COVID-19) pandemic, but its characteristics are not well understood, especially as applied to infectious disease epidemiology. The present study aims to be a practical guide to monitoring the doubling time of infectious diseases. Via simulation exercise, we clarify the epidemiological characteristics of doubling time, allowing possible interpretations. We show that the commonly believed relationship between the doubling time and intrinsic growth rate in population ecology does not strictly apply to infectious diseases, and derive the correct relationship between the two. We examined the impact of varying (i) the growth rate, (ii) the starting point of counting cumulative number of cases, and (iii) the length of observation on statistical estimation of doubling time. It was difficult to recover values of growth rate from doubling time, especially when the growth rate was small. Starting time period is critical when the statistical estimation of doubling time occurs during the course of an epidemic. The length of observation was critical in determining the overall magnitude of doubling time, and when only the latest 1-2 weeks' data were used, the resulting doubling time was very short, regardless of the intrinsic growth rate r. We suggest that doubling time estimates of infectious disease epidemics should at a minimum be accompanied by descriptions of (i) the starting time at which the cumulative count is initiated and (ii) the length of observation.

Multiple Instance Learning for Viral Pneumonia Chest X-ray Classification

At the end of 2019 anew coronavirus, SARS-CoV-2, was identified as responsible for the lung infection, now called COVID-19 (coronavirus disease 2019). Since then there has been an exponential growth of infections and at the beginning of March 2020 the WHO declared the epidemic a global emergency. An early diagnosis of those carrying the virus becomes crucial to contain the spread, morbidity and mortality of the pandemic. The definitive diagnosis is made through specific tests, among which imaging tests play an important role in the care path of the patient with suspected or confirmed COVID-19. Patients with serious COVID-19 typically experience viral pneumonia. This paper uses the Multiple Instance Learning paradigm to classify pneumonia X-ray images, considering three different classes: radiographies of healthy people, radiographies of people with bacterial pneumonia and of people with viral pneumonia. The proposed algorithms, which are very fast in practice, appear promising especially if we take into account that no preprocessing technique has been used. © 2022 CEUR-WS. All rights reserved.

Multi-label topic classification for COVID-19 literature annotation: A BioBERT-based feature enhancement approach

With the rapid expansion and exponential growth of biomedical literatures, especially in the current environment of COVID-19 pandemic, it is urgent to explore an effective technology to automatically manage and categorize massive information for biomedical texts. The wide application and powerful performance of BERT have shown promising results in the field of natural language processing. Thus, we first choose the improved pre-trained language models CovidBERT and BioBERT as the basis, from the best performance of which further enhances semantic representation of with extra title information. Finally, a novel feature enhancement method is proposed to exploit and integrate the distribution of label information effectively. The experimental results show that our model achieves an instance-based F1 score, precision and recall of 93.94%, 93.5% and 94.38% in the task of multi-label topic classification from track 5 BioCreative VII. © VDE VERLAG GMBH - Berlin - Offenbach.

Predicting the Dynamics of Covid-19 Propagation in Azerbaijan based on Time Series Models

The study is dedicated to developing an econometric model that can be used to make medium-term forecasts about the dynamics of the spread of the coronavirus in different countries, including Azerbaijan. We examine the number of COVID-19 cases and deaths worldwide to understand the data's intricacies better and make reliable predictions. Though it’s essential to quickly obtain an acceptable (although not perfect) prediction that shows the critical trends based on incomplete and inaccurate data, it is practically impossible to use standard SIR models of the epidemic spread. At the same time the similarity of the dynamics in different countries, including those which were several weeks ahead of Azerbaijan in the epidemic situation, and the possibility of including the heterogeneity factors into the model allowed as early as March 2020 to develop the extrapolation working relatively well on the medium-term horizon. The SARS-CoV-2 virus, which causes COVID-19, has affected societies worldwide, but the experiences have been vastly different. Countries' health-care and economic systems differ significantly, making policy responses such as testing, intermittent lockdowns, quarantine, contact tracing, mask-wearing, and social distancing. The study presented in this paper is based on the Exponential Growth Model method, which is used in statistical analysis, forecasting, and decision-making in public health and epidemiology. This model was created to forecast coronavirus spread dynamics under uncertainty over the medium term. The model predicts future values of the percentage increase in new cases for 1–2 months. Data from previous periods in the United States, Italy, Spain, France, Germany, and Azerbaijan were used. The simulation results confirmed that the proposed approach could be used to create medium-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Azerbaijan, the deviation of the predicted total number of confirmed cases from the actual number was within 3-10 percent. Based on March statistics on the spread of the coronavirus in the US, 4 European countries: Italy, Spain, France, Germany (most susceptible to the epidemic), and Azerbaijan, it was shown how the trajectory would deviate exponentially from a shape;a trial was carried out to identify and assess the key factors that characterize countries. One of the unexpected results was the impact of quarantine restrictions on the number of people infected. We also used the medium-term forecast set by the local government to assess the adequacy of health systems. © 2022, World Scientific and Engineering Academy and Society. All rights reserved.