Psychological Impact of Coping Strategies and Nurse Performance during the Covid-19 Pandemic at Naval Central Hospital Dr. Ramelan Surabaya

Background: Coronavirus disease (Covid-19) has become an epidemic that creates a psychological burden for nurses who provide nursing care in health services. The psychological condition of nurses who are not good will affect anxiety, coping strategies, and performance when carrying out nursing care. Purpose: This study aims to describe the psychological impact on coping strategies and the performance of nurses in health services. Methods: The research design used observational analytic with a cross sectional approach. The population used were nurses who worked in the COVID-19 isolation room at the Naval Central Hospital Dr. Ramelan Surabaya with 54 nurses. The sampling technique is used with total sampling, that is, the entire population is used as a sample in the study. Results: The results of the Spearman Rho correlation test have a relationship between the psychological impact of anxiety and the nurse's strategy in dealing with COVID-19 patients (ρ-value = 0.001);there is no relationship between the psychological impact of anxiety and the performance of nurses in dealing with covid 19 patients (ρ-value = 0.08);there is no relationship between the psychological impact of depression and the performance of nurses in dealing with covid 19 patients (ρ-value = 0.064). and there is a relationship between the psychological impact of stress and the performance of nurses in dealing with covid 19 patients (ρ-value = 0.0124);and there is a relationship between the psychological impact of stress and the performance of nurses in dealing with covid 19 patients (ρ-value = 0.0124). Conclusion: Psychological impacts do not always affect the performance of nurses, this can happen because nurses have good coping skills, plus nurses have attended seminars and training so that nurses' knowledge and skills have increased. Nurses have also been vaccinated, so nurses' anxiety and fear in treating COVID patients has decreased. © 2022 The authors.

Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. The FIRE 2022 track on Information Retrieval from Microblogs during Disasters (IRMiDis) focused on two important tasks – (i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets. © 2022 Copyright for this paper by its authors.

Parents' Psychological Aspect in Caring for the Child During COVID-19 Pandemic

Introduction: COVID-19 is unprecedented, especially for parents with children aged 2-12 years old who are prone to infection due to their activities. The psychological condition of parents will be affected as they are worried about their children being infected with COVID-19. Methods: This study aims to identify and analyse the psychological aspects of parents who are caring for children during the COVID-19 pandemic. Results: The results showed that as many as 236 parents (80.9%) showed an emotional anxiety response. The social reaction to the COVID-19 situation was more likely to be a positive response (73.8%) rather than a negative response. Conclusion: The reactions that occur in parents who have pre-school and school-aged children regarding the COVID-19 were anxiety and worry. Reactions arise due to the information obtained being less verifiable, namely social media. © 2021, Faculty of Nursing, Universitas Airlangga. All rights reserved.

Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. Over the last several years, the track on Information Retrieval from Microblogs during Disasters (IRMiDis), organized as part of the FIRE conference series, has provided annotated datasets for developing ML/NLP techniques for utilizing microblogs for various practical tasks that would help authorities better deal with disaster situations. In particular, the FIRE 2022 IRMiDis track focused on two important tasks-(i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets. © 2022 Owner/Author.

PhishGuard-An Automatic Web Phishing Detection System

People are being compelled to adopt a "work from home"strategy because of the COVID-19 outbreak. In today's world, the Internet has evolved into a powerful tool for social connection. People's reliance on digital platforms creates opportunities for deception. Phishing websites are one of the types of internet security issues that target human vulnerabilities rather than software flaws. It is defined as the act of impersonating someone else to steal sensitive information such as usernames and passwords from online users. In this research, we provide an intelligent system for identifying phishing websites that work as an extension to an internet browser and automatically warn the user when a phishing website is detected. © 2022 IEEE.

COVID-19 COMPLICATIONS AND SUGGESTED MEASURES: MODERN TOOLS FOR INTERVENING PANDEMIC

Viral diseases are the most devastating health concern worldwide. Outbreaks of coronavirus (CoVs)-related acute respiratory diseases are responsible for the massive health/socio-economic breakdown in the last two decades including the Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), the third reported spillover SARS-CoV-2 from an animal coronavirus to humans. After the H1N1 pandemic influenza (2009), SARS-CoV-2 (novel-beta coronavirus) causing COVID-19 has stretched across 215 countries in 5 major continents with 200,523,190 confirmed cases (4 August 2021;https://www.worldometers.info/coronavirus/). COVID-19 patients had cough, fever, dyspnea, headache, and respiratory failure, as well as shock, acute respiratory distress syndrome, and sepsis in severe instances. Independent of two preceding epidemics, SARS (2002) and MERS (2012), a knowledge gap about the emerging medical manifestations as well as complications of SARS-CoV-2 (2019-2020) infections in humans must be filled, with a focus on immunological complications and computational genomics for forecasting/preparedness for a similar outbreak in the future. This paper aims to address aspects of this gap.

CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models. © 2022 ACM.

Overview of the FIRE 2021 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. The FIRE2021 track on Information Retrieval from Microblogs during Disasters (IRMiDis) focused on two important tasks - (i) to identify claims or fact-checkable tweets, which is the first step towards verifying information posted on social media, and (ii) to detect the vaccine-related stance of tweets related to COVID-19 vaccines. © 2021 Owner/Author.

SARS-CoV-2 Infection and Risk Stratification among Healthcare Workers in the Largest COVID-19 Dedicated Tertiary Care Hospital in Dhaka, Bangladesh

Background: The health care workers’(HCWs) are working 24/7 in managing devastating pandemic Corona virus disease19(COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) as front liner which leads them to be at highest risk for contacting infection. In Bangladesh, being a lower middle-income country and densely populated, the burden is much more on HCWs. Methods: We did a cross-sectional study with an aim to identify the prevalence, risk factors, and outcomes of SARS-CoV-2 infection among the HCWs in a COVID-19 dedicated tertiary care hospital. Statistical analysis was done in SPSS version-26. Multivariate regression analysis was done to evaluate risk factors responsible for COVID-19 infection and the severity of the COVID-19 disease. We expressed odds ratio with 95% CI, and considered the p-value of <0.05 as significant in the two-tailed test. Results: A total of 864 HCWs had participated with mean age of 34.16 ± 6.77 and 426 (49.31%) males. Among them 143 (16.55%) were tested RT-PCR positive for SARS-COV-2. Bronchial asthma/COPD and Hypertension were the most common co-morbidities with 23 (16.08%) for each. About 102 (71.33%) of the RT-PCR positive HCWs became symptomatic. Fever, cough and myalgia were the most common symptoms 84(82.35%), 67(65.69%) and 52(50.98%) respectively. Multivariate regression analysis revealed hypertension, gout, and working in the COVID-19 confirmed ward had a significant odds ratio for getting infected with SARS-CoV-2 [95% CI, p-value 1.91 (1.08-3.41), 0.027;5.85 (1.33-25.74), 0.020;and 1.83 (1.10-3.03), 0.019]. Bronchial asthma/COPD and gout found to be risk factors for moderate to severe COVID-19 disease [95% CI, p-value 3.04 (1.01-9.21), 0.049 and 23.38 (3.42-159.72), 0.001]. Hospitalization rate was 12(85.7%), and 3(100%) and median hospital stays were 11 (5.5-15), and 20 (7-30) days for moderate and severe diseases respectively. Outcome was uneventful without any ICU admission and death. Conclusion: HCWs working in the COVID-19 confirmed ward are at increased risk of infection with SARS-COV-2. Some co-morbidities like hypertension and gout are important risk factors for contacting SARS-COV-2 infection. Bronchial asthma/COPD and gout favors disease severity.

Clinico-radiological progression in COVID-19 patients: Study in a tertiary care hospital

Background: Coronavirus disease 19 (COVID-19) caused by a severe acute respiratory syndrome coronavirus-2 belonging to the Coronaviridae family has caused a global pandemic. As it has emerged as a newer disease, there is a lack of information in many aspects of it. Materials and Methods: We tried to study the progression in the COVID-19 patients in terms of their clinical, laboratory, and X-rays as a radiological modality, for that, we did a single-centered retrospective observational study in 159 laboratory-confirmed COVID-19 positive patients with sample size being duration dependent. Results: We found statistically significant correlation between clinical parameters and lung involvement based on chest X-ray (CXR) scoring and also temporal variation as the disease progression occurs. Conclusion: Along with clinical and laboratory parameters, CXRs can be used as a useful bedside, inexpensive, and easily available radiological tool for assessment in resource-poor settings where high-resolution computed tomography scans are not feasible.

Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach

We propose VADEC, a multi-task framework that exploits the correlation between the categorical and dimensional models of emotion representation for better subjectivity analysis. Focusing primarily on the effective detection of emotions from tweets, we jointly train multi-label emotion classification and multi-dimensional emotion regression, thereby utilizing the inter-relatedness between the tasks. Co-training especially helps in improving the performance of the classification task as we outperform the strongest baselines with 3.4%, 11%, and 3.9% gains in Jaccard Accuracy, Macro-F1, and Micro-F1 scores respectively on the AIT dataset [17]. We also achieve state-of-the-art results with 11.3% gains averaged over six different metrics on the SenWave dataset [27]. For the regression task, VADEC, when trained with SenWave, achieves 7.6% and 16.5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset [5] for the Valence (V) and Dominance (D) affect dimensions respectively. We conclude our work with a case study on COVID-19 tweets posted by Indians that further helps in establishing the efficacy of our proposed solution. © 2021 ACM.

The roles of knowledge and perceptions in COVID-19 transmission prevention behavior

Introduction: COVID-19 is an infectious disease caused by novel Corona virus strain and transmitted in community. The present research aimed to find out about the relationships of knowledge and perceptions related to COVID-19 transmission prevention behavior among ticketing workers of a ferry operator. Methods: This research employed quantitative methods with cross-sectional research design and chi-square analysis approach. The research sample consisted of 99 respondents who were ticketing workers of ferry operators within the working area of the Port of Merak, Banten. Results: The research results showed that 30.3% of the respondents had poor and remaining 69.7% had good COVID-19 transmission prevention behavior. As many as 66.7% of the respondents had good knowledge, while 42.4 % had constructive perception of COVID-19 transmission prevention behavior. The analysis test showed that there were significant relationships between knowledge and perceptions related to COVID-19 transmission prevention behavior. Managers are suggested to improve workers’ knowledge and perceptions on COVID-19 transmission prevention. Conclusion: Workers with good knowledge were twice as likely to have good COVID-19 transmission prevention behavior. © 2021 UPM Press. All rights reserved.

Changing epidemiology of SARS-CoV in the context of COVID-19 pandemic.

SARS-CoV-2 is a new form of ß-coronavirus that has been recently discovered and is responsible for COVID 19 pandemic. The earliest infection can be traced back to Wuhan, China. From there it has spread all over the world. Keeping in view the above perspective, an attempt is made in order to find out the epidemiological pattern of COVID 19 pandemic, if any, in different geo-climatological regions of the world in terms of case incidence and mortality. This study is also an endeavor to review and analyze the gradual changes of the genetic makeup of SARS-CoV from evolutionary and epidemiological perspectives. The raw data of COVID-19 cases and death incidences were collected from the World Health Organization (WHO) website from the time period: 1st April to 6th April, 2020. The data that are utilized here for general and Case fatality rate (CFR) based analysis. Western pacific region, European region and Americas have the greatest number of infected cases (P < 0.001); whereas deaths have been found to be significantly higher in Europe (P < 0.001). Total number of confirmed cases and deaths in south-east Asia are comparatively lower (P < 0.001). Case fatality rate (CFR) has also found significant for European region. SARS-CoV-2 is considered to be a strain of SARS-CoV that has a high rate of pathogenicity and transmissibility. Result indicated that the European region has been affected mostly for both cases and death incidences. The novel mutations in SARS-CoV-2 possibly increase the virus infectivity. Genetic heterogeneity of this virus within the human population might originate as the representatives of naturally selected virus quasispecies. In this context, the presence of the asymptomatic individuals could be a significant concern for SARS-CoV-2 epidemiology. Further studies are required to understand its genetic evolution and epidemiological significance.