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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
ABSTRACT
The COVID-19 pandemic has forced people worldwide to modify their daily activities, including travel plans. To help individuals make informed decisions about visiting public places, Cheng [2] first proposed a real-time COVID-19 risk assessment system called RT-CIRAM and implemented prototypes for two U.S. metropolitan locations. The system calculates a COVID-19 risk score and categorizes the risk levels into high, medium, and low, recommends the safe travel destination using the users' location and the specified distance the user is willing to travel, thereby helping users make informed decisions about their travel plans. © 2023 ACM.
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The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.
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Due to the Covid-19 pandemic, two problems arose. Students lacked 1) social opportunities and 2) motivation to maintain their schedules, e.g., studying or relaxing, as their work-life balance disappeared. Thus, we designed a social companion robot, Bulb, that helped students cycle through daily activities with subtle cues, i.e., light, gaze, and movements. Bulb's "head"would light up with different colors or it gazes at different parts of the room, e.g., at the laptop to hint at studying or wiggling to suggest a small break. Five students evaluated Bulb through at-home use, which demonstrated that Bulb was seen as a "living being"and students were responsive to its social cues, like following Bulb's gaze, resulting in a higher awareness and follow-through of students' schedules. Our contribution is in designing a social companion robot that subtly persuaded students through light and anthropomorphic social cues, helping them maintain their daily schedule during the pandemic. © 2023 Owner/Author.
ABSTRACT
COVID-19 lockdown has provided a unique example of a sudden and significant reduction of human presence in a rural area, especially in villages with high tourist pressure. We used camera-trapping to investigate the effect of reduction of human activity due to COVID-19 lockdown in a rural area on activity patterns of species considered urban exploiters and urban adapters. The activity patterns of both predators changed slightly and activity peaks shifted without significant differences in temporal niche overlap. The stone marten, an urban exploiter, had a bimodal activity pattern and shifted the main peak of its activity earlier during COVID-19 lockdown. It was quick to respond to the decrease in human presence in the first half of the night by increasing activity in that time. Meanwhile, the red fox, an urban adapter, showed larger variation in activity patterns and shifted summer and autumn-winter activity peaks to later at night or even early morning. These changes resulted in slight differences in the overlap of activity rhythms of both species. Stone marten and red fox have adapted their activity to avoid human encounter and are active mainly at night, responding by a small extent to reduction of human presence during COVID-19 lockdown, which occurs mainly during the day. However, COVID-19 lockdown and lower human mobility may partially reduce interspecific competition induced by anthropogenic activities in rural areas.
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The pandemic of Covid-19 requires people in any profession to do large-scale social restrictions, this also lead in financial/external auditors experiencing difficulties in conducting audits in the common daily activity, which is by visiting clients physically to make observations. In this condition, online observation using remote audit become one of the solution. The purpose of this study is to analyze the effectiveness and efficiency of external auditors in transition to remote auditing due to the Covid-19 Pandemic. The data collection technique is using primary data from questionnaire. We distribute questionnaire to Public Accounting Firms located in DKI Jakarta using simple random sampling method technique. The method for data analysis is using partial least squares conducted with Software of SmartPLS 3. The result of this study indicates that remote audit efficiency and remote audit efficiency have positive and significant effect on audit quality. Meanwhile, institutional support has no significant effect on audit quality. © 2022 IEEE.
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The outbreak of the pandemic, caused by Coronavirus Disease 2019 (COVID-19), has affected the daily activities of people across the globe. During COVID-19 outbreak and the successive lockdowns, Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously. Several studies used Sentiment Analysis (SA) to analyze the emotions expressed through tweets upon COVID-19. Therefore, in current study, a new Artificial Bee Colony (ABC) with Machine Learning-driven SA (ABCML-SA) model is developed for conducting Sentiment Analysis of COVID-19 Twitter data. The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made upon COVID-19. It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors. For identification and classification of the sentiments, the Support Vector Machine (SVM) model is exploited. At last, the ABC algorithm is applied to fine tune the parameters involved in SVM. To demonstrate the improved performance of the proposed ABCML-SA model, a sequence of simulations was conducted. The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches. © 2023 Tech Science Press. All rights reserved.
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"Human Activity Recognition" (HAR) refers to the ability to recognise human physical movements using wearable devices or IoT sensors. In this epidemic, the majority of patients, particularly the elderly and those who are extremely ill, are placedin isolation units. Because of the quick development of COVID, it's tough for caregivers or others to keepan eye on them when they're in the same room. People are fitted with wearable gadgets to monitor them and take required precautions, and IoT-based video capturing equipment is installed in the isolation ward. The existing systems are designed to record and categorise six common actions, including walking, jogging, going upstairs, downstairs, sitting, and standing, using multi-class classification algorithms. This paper discussed the advantages and limitations associated with developing the model using deep learning approaches on the live streaming data through sensors using different publicly available datasets. © 2022 IEEE
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COVID-19 infection has been a major topic of discussion on social media platforms since its pandemic outbreak in the year 2020. From daily activities to direct health consequences, COVID-19 has undeniably affected lives significantly. In this paper, we especially analyze the effect of COVID-19 on education by examining social media statements made via Twitter. We first propose a lexicon related to education. Then, based on the proposed dictionary, we automatically extract the education-related tweets and also the educational parameters of learning and assessment. Afterwards, by analyzing the content of the tweets, we determine the location of each tweet. Then the sentiments of the tweets are analyzed and examined to extract the frequency trends of positive and negative tweets for the whole world, and especially for countries with a significant share of COVID-19 cases. According to the analysis of the trends, individuals were globally concerned about education after the COVID-19 outbreak. By comparing between the years 2020 and 2021, we discovered that due to the sudden shift from traditional to electronic education, people were significantly more concerned about education within the first year of the pandemic. However, these concerns decreased in 2021. The proposed methodology was evaluated using quantitative performance metrics, such as the F1-score, precision, and recall. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
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For the past few years, the world has seen a paradigm shift in the way of life. Daily activities of working, learning, etc. were shifted to an online mode so that the spread of the covid virus can be contained. During this time, usage of social media apps was at an all-time high since it was the only way people could socialize. The clubhouse was one such social media app that gained popularity during the lockdown and showed exponential growth in terms of user engagement. But, it was observed that from the survey conducted among 33 users, almost 82% of users showed reluctance in using Clubhouse over time. 84.8% of users welcomed the need for a better User Experience for the platform. An average of 64.2% of users reported different User Experience (UX) related issues in the app. In this paper, the UX side of the app is studied and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Covid-19 is more likely to spread in campus than it in other places because students live together without masks. In this case, it is necessary to take nucleic acid tests in a unified time regularly. To make nucleic acid tests efficient and convenient to manage students and the testing time, this article would apply queuing theory to design a nucleic acid tests queuing system by using the data from Sanda University in April 2022. According to the special conditions on campus, such as course schedule, students' daily activities, and campus management, students would be grouped by several management styles. The system would calculate the start time and waiting time for each group and would strive to take nucleic acid tests in an orderly manner with minimal waiting time. © 2022 IEEE.
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Early career faculty are undergoing a stressful transition period and actively negotiating their professional identity. The COVID-19 pandemic has changed the daily activities in early career faculty's personal and professional lives and thus complicated the negotiation process. This study explores how engineering faculty members redefine and reconceptualize what it means to be in their early career during the COVID-19 pandemic. Through an emergent qualitative coding technique, we identified two themes: 1) the blurring of personal and professional boundaries, and 2) the renegotiation of different identities. The findings offer insights into how to better support early career faculty and allow them to balance these different dimensions of their academic identities. © 2022 IEEE.
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During the COVID-19 pandemic, there was an undeniable acceleration of time when social distancing measures were in place. Digital natives are the population group that was most accustomed to online tools, which allowed them to stay in contact and carry out their daily activities during the most critical stage of the pandemic. An important element that strengthens interaction in digital media is trust, both in the sources of information and in the medium. For this reason, quantitative research was developed in which the associations between trust in social networks and other digital communication tools in Mexican youth were analyzed. The study was conducted with students from public and private universities during 2021, when strict confinement measures still prevailed in Monterrey, Nuevo León. The study confirms some of the findings of other research in which trust is a fundamental variable in effective communication through digital channels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Throughout the years, healthcare has been one of the privileged areas to apply the information discovery process, empowering and supporting medical staff on their daily activities. One of the main reasons for its success is the availability of medical expertise, which can be incorporated in training models to reach higher levels of performance. While this has been done painfully and manually, during the preparation step, it has become hindered with the advent of AutoML. In this paper, we present the automation of data preparation and feature engineering, while exploring domain knowledge represented through extended entity-relationship (EER) diagrams. A COVID-19 case study shows that our automation outperforms existing AutoML tools, such as auto-sklearn [4], both in quality of the models and processing times. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The outbreak of the COVID-19 pandemic forced a need to create screening tests to diagnose the disease. To answer this challenge, this paper introduces the support methodology for COVID-19 early detection based on wearable and machine learning likewise on two various cohorts. We compare the level of detection of the COVID-19 disease, Influenza, and Healthy Control (HC) thanks to the usage of machine learning classifiers likewise changes in heart rate and daily activity. The features obtained as the parameters of the ratio of heart rate to the variable of the number of steps proved to have the highest statistical importance. The COVID-19 cases versus HC were possible to be distinguished with 0.73 accuracy by the XGBoost algorithm, whereas COVID-19 cases, Influenza vs. HC were able to be differentiated on similar level of accuracy: in 0.72 by Support Vector Machine. The multiclass classification between the cases achieved a 0.57 F1-score for three classes by XGBoost. For early diagnosis, this solution could serve as an extra test for clinicians during the pandemic, and the result shows which metric could be useful for creating the machine learning model. © 2022 IEEE.
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In recent years, people have been requiring new livelihoods that allow them to have enough economical resources for the development of their daily activities, considering the problematic that COVID-19 has brought to their lives. The objective of this research was to analyze machine learning algorithms such as Decision Tree, Random Forest, Naive Bayes, Logistic Regression and Vector Support Machine, in order to identify the risk level to fall into poverty for a person in Perú, basing the analysis on the National Household Survey (NHS) that the National Institute of Statistics and Informatics (NISI) provided on 2020. The methodology was presented in four stages, organization and structuring of the database, analysis and identification of the variables, application of the learning algorithms and evaluation of the performance of the aforementioned algorithms. Python programming language and the STATA software allowed the exploration of 91,315 registers and 33 variables. Results showed that the Decision Tree algorithm has an accuracy of 98%, while other algorithms are below the indicated range, so dynamism is expected in the application of this algorithm for socioeconomic areas that can be materialized through a permanent evaluation and analysis platform that helps to focus strategies and proposals for the benefit of the population with economic limitations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
For the past few years, the world has seen a paradigm shift in the way of life. Daily activities of working, learning, etc. were shifted to an online mode so that the spread of the covid virus can be contained. During this time, usage of social media apps was at an all-time high since it was the only way people could socialize. The clubhouse was one such social media app that gained popularity during the lockdown and showed exponential growth in terms of user engagement. But, it was observed that from the survey conducted among 33 users, almost 82% of users showed reluctance in using Clubhouse over time. 84.8% of users welcomed the need for a better User Experience for the platform. An average of 64.2% of users reported different User Experience (UX) related issues in the app. In this paper, the UX side of the app is studied and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The breakout of the COVID-19 pandemic shifted people's daily activities from in-person to video-mediated ones. Many people with hearing loss encounter cognitive overload due to ineffective visuals of the videoconferencing interface and therefore find meeting contents difficult to comprehend. This research incorporates a participatory design methodology to investigate the Deaf and Hard of Hearing (DHH) users' tacit needs. DHH users demonstrated ways of mitigating their hardships in the workshop, such as emphasizing the visual hierarchy or assigning visual cues to fixed positions. These findings are used in developing design directions for creating a more inclusive online environment. © 2022 Owner/Author.
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Human daily activities, such as working, eating out, and traveling, play an essential role in contact tracing and modeling the diffusion patterns of the COVID-19 pandemic. However, individual-level activity data collected from real scenarios are highly limited due to privacy issues and commercial concerns. In this paper, we present a novel framework based on generative adversarial imitation learning, to generate artificial activity trajectories that retain both the fidelity and utility of the real-world data. To tackle the inherent randomness and sparsity of irregular-sampled activities, we innovatively capture the spatiotemporal dynamics underlying trajectories by leveraging neural differential equations. We incorporate the dynamics of continuous flow between consecutive activities and instantaneous updates at observed activity points in temporal evolution and spatial transformation. Extensive experiments on two real-world datasets show that our proposed framework achieves superior performance over state-of-the-art baselines in terms of improving the data fidelity and data utility in facilitating practical applications. Moreover, we apply the synthetic data to model the COVID-19 spreading, and it achieves better performance by reducing the simulation MAPE over the baseline by more than 50%. The source code is available online: https://github.com/tsinghua-fib-lab/Activity-Trajectory-Generation. © 2022 ACM.
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Technology plays an important role into the life of older people. With the increase of age, they are experiencing physical and cognitive frailties and they require assistance for the management of their daily activities. In this sense, digital technologies could offer a holistic ecosystem which could empower their daily life 24 h decreasing the caregiver burden. Multi-domains researchers are joining their efforts to propose a selection of services. In this context, this paper introduces the large scale pilot Pharaon project, pointing out the attention on the Italian pilot site. Within the Italian pilot, a personalized and integrated care service was and will be investigated in the forthcoming years to meet the challenge of older population. Particularly, the paper introduces the methodology and the actions performed to face the covid-19 pandemic which affect the first stage of the process, the service domains, and the methodology applied. Additionally, the paper presents and discusses the key performance indicators related to impact, business, social and clinical domains and how the technology is used within the Italian pilot to support the population during the pandemic emergency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.