Classifications of COVID-19 Variants Using Rough Set Theory

Since January 2020, the corona epidemic has created havoc worldwide. Although this virus has been mutated many times, the recent variant is more fatal for humans. Increasing active and death cases in the globe as well as in our country affect the psychological well-being of the people. India has experienced all variants including Alpha variant (B.1.1.7), Delta variant (B.1.617.2), and Omicron variant (B.1.1.529). All variants have some common symptoms along with extended symptoms. In this paper, we propose a rule base to classify and predict the variants of COVID-19 using a rough set approach. Our approach works for the elimination of redundant symptoms to create effective reduct, core, and selection of important symptoms to maintain the accuracy in a rule base. Our rules are validated to computer-generated data with 90% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Classifications of COVID-19 Variants Using Rough Set Theory

Since January 2020, the corona epidemic has created havoc worldwide. Although this virus has been mutated many times, the recent variant is more fatal for humans. Increasing active and death cases in the globe as well as in our country affect the psychological well-being of the people. India has experienced all variants including Alpha variant (B.1.1.7), Delta variant (B.1.617.2), and Omicron variant (B.1.1.529). All variants have some common symptoms along with extended symptoms. In this paper, we propose a rule base to classify and predict the variants of COVID-19 using a rough set approach. Our approach works for the elimination of redundant symptoms to create effective reduct, core, and selection of important symptoms to maintain the accuracy in a rule base. Our rules are validated to computer-generated data with 90% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 Survivor: Design and Evaluation of a Game to Improve Students’ Experience During Social Isolation

In socio-economic crises, such as the recent Covid-19 pandemic, it is crucial to enhance children’s understanding of the new situation and their choices to protect their physical and mental health. In this paper, we discuss the design of a serious choice-driven simulation game, called “Covid-19 Survivor”, as a means to empower students’ awareness of pandemic risks and consequences through decision making and system analysis. The online game simulates the daily routine of a school student through a system of available choices and consequences to five game fields. We qualitatively evaluated the game in a classroom setting with twenty-six 13 years old students and one IT teacher. The preliminary results demonstrate its potential to influence students’ perceptions and knowledge about the pandemic as well as to enhance their decision-making and systems thinking skills. © 2021, Springer Nature Switzerland AG.

Challenges of data collection on mturk: A human-AI joint face-matching task

During the COVID-19 pandemic, many human-subject studies have stopped in-person data collection and shifted to virtual platforms like Amazon Mechanical Turk (MTurk). This shift involves important considerations for study design and data analysis, particularly for studies involving behavioral assessment and performance with technology. We report on lessons learned from a recent study that used MTurk for a face-matching task with an open-source AI. Participants received $5 compensation for completing a 45-minute session that included questionnaires. To help address data validity issues, Qualtrics fraud-detection features (i.e., reCAPTCHA, ID-Fraud), trap-items (e.g., Respond with Often), and a modified-batch-randomization-process were employed. Participants' accumulative accuracy and response rates were also assessed. Out of 272 participants, 121 passed the data inclusion criteria. The questionnaires' reliability was within range (average 0.78) for the healthy dataset. Accumulative accuracy in the face-matching task decreased approximately halfway through the task. Subsequent data inspection revealed that almost half of the participants spent longer than 20 seconds and up to 12 minutes on a random image pair. It is possible that participants were interrupted during the study or they elected to take unscheduled breaks. Environmental factors that were easier to control during in-person laboratory studies now require built-in controls for virtual study environments. We learned that: (1) it is imperative to monitor performance measures over time for each participant;(2) the study duration may need to be kept shorter on virtual platforms compared to in-person studies;(3) an optional, planned break during the task might help prevent other unplanned breaks. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

Decision-making algorithms for learning and adaptation with application to COVID-19 data.

This work focuses on the development of a new family of decision-making algorithms for adaptation and learning, which are specifically tailored to decision problems and are constructed by building up on first principles from decision theory. A key observation is that estimation and decision problems are structurally different and, therefore, algorithms that have proven successful for the former need not perform well when adjusted for the latter. Exploiting classical tools from quickest detection, we propose a tailored version of Page's test, referred to as BLLR (barrier log-likelihood ratio) test, and demonstrate its applicability to real-data from the COVID-19 pandemic in Italy. The results illustrate the ability of the design tool to track the different phases of the outbreak.