COVID-19 epidemic anxiety, mental stress, and sleep disorders in developing country university students

COVID-19 damaged the quality of sleep and mental stamina worldwide despite public health initiatives. Problems with sleep can damage health and academic performance, thus university students should know their frequency and causes. This study explored university students' COVID-19 Anxiety, Mental Stress, and Sleep Disorders. The internet-deployed transversal analysis includes 443 Indian and Ethiopian students from April 1 to 13, 2021. After creating a Google form link, respondents received the survey via WhatsApp, E-mail, Telegram, and others. Pittsburgh Sleep Quality Index examined student sleep concerns. Descriptive and inferential studies estimated sleep disruption frequency and causes. Logistic regression and chi-squared testing found sleep problems in Indian and Ethiopian university students. The researcher examined SPSS 25 data. 54.7% had sleep difficulties. Being female [Conditional Odds Ratio = 4.32, 95 percent Confidence interval (Lower-1.807)–(Upper-10.370)], smoking [2.81, 95 percent CI (Lower-1.609)–(Upper-4.920)], spending 14 days in quarantine [1.87, 95 percent CI (Lower-1.042)–(Upper-3.373)], and having a family member with COVID-19 [1.94, 95 percent CI (Lower-1.anxiety [Conditional Odds Ratio = 4.01, 95 percent CI (Lo Ethiopian and Indian pupils sleep poorly. Female gender, smoking, 14-day quarantine, and sleep troubles were connected to COVID-19 dread, COVID-19-infected family member, viral exposure, depression, anxiety, and stress in Indian and Ethiopian university students. Interventions should increase university students' sleep.

Knowledge, attitudes, and practices of palestinian students toward covid-19: A cross-sectional study during the first wave of the covid-19 pandemic

The knowledge, attitudes, and practices (KAP) of students concerning COVID-19 have an impact on their adherence to preventative procedures. This study aimed to investigate the knowledge, attitudes, and practices toward COVID-19 among school students. A descriptive cross-sectional design was used to assess the knowledge, practice, and attitudes of 600 students toward COVID-19. Study participants were recruited from schools between July and August 2020. The mean score of knowledge was 7.60 +/- 4.63, which reflects an unacceptable level of knowledge about COVID-19. Knowledge scores were significantly different across gender (p = 0.017), age groups (p = 0.008), the presence of a family member working in the health sector (p < 0.001), and economical level of family (p < 0.001). Being female, students aged 15-18, and those from high-income families obtained significantly higher knowledge scores. About 68.6% of the students possessed negative attitudes toward the successful control of COVID-19. This study found that more than half of students committed preventive procedures such as avoiding gatherings and practicing good hand hygiene during the COVID-19 pandemic. However, only 28.5% confirmed wearing a face mask when leaving their homes. This study affirms the necessity for immediate health initiatives aimed at increasing COVID-19 knowledge and, thereby, more positive attitudes toward preventative procedures. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Mathematical modelling for prediction of spread of corona virus and artificial intelligence/machine learning-based technique to detect COVID-19 via smartphone sensors

COVID-19 is a novel corona virus which is infectious and communicable disease and it is originated from Wuhan, China. As the virus is mutating, the world is suffering from its spread again and again. However, the spread of communicable diseases can be predicted in advance so the proper preventive measures can be taken before it become life-taking. In this paper, mathematical model (SEIR) for the prediction of infectious diseases, which is modification of conventional SIR model is described and modelled which can be used to predict the cases in advance. A novel framework to detect COVID-19 from home is also proposed using artificial intelligence, machine learning and smartphone embedded sensors. The various smartphone embedded sensors such as proximity sensor, light sensor, accelerometer, gyroscope and fingerprint sensors are used to read the symptoms or activity and scan the CT images, and can be used to detect COVID-19.

Understanding COVID-19 response by twitter users: A text analysis approach.

COVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter has emerged as a major platform for public interactions, giving opportunity to researchers for understanding public response to the outbreak. The researchers analyzed 100,000 tweets with hashtags #coronavirus, #coronavirusoutbreak, #coronavirusPandemic, #COVID19, #COVID-19, #epitwitter, #ihavecorona, #StayHomeStaySafe, #TestTraceIsolate. Programming languages such as Python, Google NLP, and NVivo are used for sentiment analysis and thematic analysis. The result showed 29.61% tweets were attached to positive sentiments, 29.49% mixed sentiments, 23.23 % neutral sentiments and 18.069% negative sentiments. Popular keywords include "cases", "home", "people" and "help". We identified "30" such topics and categorized them into "three" themes: Public Health, COVID-19 around the world and Number of Cases/Death. This study shows twitter data and NLP approach can be utilized for studies related to public discussion and sentiments during the COVID-19 outbreak. Real time analysis can help reduce the false messages and increase the efficiency in proving the right guidelines for people.

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm.

The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

Prevalence of depression, anxiety and stress during the COVID-19 pandemic: a cross-sectional study among Palestinian students (10-18 years).

BACKGROUND: The COVID-19 pandemic considers a threat to students' well-being and mental health. The current descriptive cross-sectional study aims to identify psychological distress among school students during the lockdown period. METHODS: This study was carried out in a sample of 420 primary and secondary school students from June 10 to July 13, 2020, in the Gaza Strip in Palestine. Data was collected using an online questionnaire that included informed consent, socio-demographic questions, and a psychometric scale (DASS-21). RESULTS: The results revealed that most students experienced moderate to severe levels of anxiety (89.1%) and depression (72.1%), whereas less than half of them (35.7%) experienced moderate to severe stress. Stress, anxiety and depression scores were significantly different across gender, age groups, family size, and family's economic status. The results showed that gender (ß = -0.174, p < 0.001), age (ß = -0.155, p = 0.001) and economic level of family (ß = -0.147, p = 0.002) were negative predictors correlated with stress. Family size (ß = 0.156, p = 0.001) played a positive role in stress. It was found that gender (ß = -0.105, p = 0.031), age (ß = -0.135, p = 0.006) and economic level of family (ß = -0.136, p = 0.005) were negative predictors correlated with anxiety, whereas family size (ß = 0.139, p = 0.004) played a positive role in anxiety. For depression, gender (ß = -0.162, p = 0.001), age (ß = -0.160, p = 0.001) and economic level of family (ß = -0.131, p = 0.007) were negative predictors correlated with depression, whereas family size (ß = -0.133, p = 0.006) was found to be a positive predictor. Concerns about the influence of COVID-19 on economic, education, and daily life were positively correlated to the levels of depression, anxiety and stress, whereas the availability of social support was negatively correlated. CONCLUSION: The development of a health protocol for influenced students is urgently needed to maintain them remain resilient during dangerous times.

Corrigendum to "The prevalence of general anxiety disorder and its associated factors among women's attending at the perinatal service of Dilla University referral hospital, Dilla town, Ethiopia, April, 2020 in Covid pandemic" [Heliyon 6 (11) (November 2020) Article e05593].

[This corrects the article DOI: 10.1016/j.heliyon.2020.e05593.].

Use of statistical analysis to monitor novel coronavirus-19 cases in Jammu and Kashmir, India

Coronavirus disease (COVID-19) has been increasing slowly and steadily in all the districts of Jammu and Kashmir, India. It is essential for the government and health management system to monitor the districts affected due to COVID-19. The main objective of this study is to ascertain and categorize the COVID-19 affected districts into real clusters based on similarities within a cluster and differences among clusters in order to imply standard operating procedures (SOPs) policies, decisions, medical facilities, etc. could be improved for reducing the risk of infection and death and optimize the deployment of resources for preventing subsequent outbreaks.

The prevalence of general anxiety disorder and its associated factors among women's attending at the perinatal service of Dilla University referral hospital, Dilla town, Ethiopia, April, 2020 in Covid pandemic.

BACKGROUND: Coronavirus is rapidly increasing in Ethiopia, and the number of perinatal service users at the hospital decreased due to the fear of contracting the virus. The mental health of a pregnant mother is vital for preventing pregnancy and birth-related complications. This study aimed to determine the magnitude and associated factors of General anxiety disorder among perinatal service users in Dilla University referral hospital, Dilla, Ethiopia. METHODS: A hospital-based cross-sectional study was conducted among 178 respondents from April 6 - May 6, 2020. The sampling technique of this study was Consecutive sampling. Data were collected using a structured interview. General anxiety disorder (GAD-7) was used to determine the outcome variable. Logistic regression analysis and adjusted odd ratio at 95% CI and p < 0.05 was used to determine the statistically significant association between general anxiety disorder and its predictors. RESULT: A total of 178 respondents with a 100% response rate were enrolled in the study. The mean income of respondents was 1500 (±700) Ethiopian birr. The overall prevalence of general anxiety disorder (GAD) was 32.2%. Living in Rural area [AOR = 0.48; 95% CI: (0.25-0.9) P = 0.02∗], Primary level of education [AOR = 0.41; 95%CI:(0.21-0.75), P = 0.03∗], poor social support [AOR = 4.3995%CI:(2.29-12.53), P = 0.001∗∗] and primigravida [AOR = 3.05; 95% CI: (1.53-6.08), P = 0.001∗∗] were variables significantly associated with general anxiety disorder at 95% confidence interval, p < 0.05. CONCLUSIONS: This study found that nearly one-third of the respondents had general anxiety disorder. Therefore, working on the mental health impact of the pandemic among perinatal service users is an urgent solution to promote their physical, mental, and psychological health of a mother and her baby.

The Current Mental Health Crisis of COVID-19 Pandemic Among Communities Living in Gedeo Zone Dilla, SNNP, Ethiopia, April 2020.

The current COVID-19 pandemic is not still controlled around the world and affects all humans' domains of day to day life. Peoples have killed themselves due to the fear of stigma by their community. This study aimed to assess the current mental health crisis of the COVID-19 pandemic among communities living in, Gedeo zone, Dilla, Ethiopia. The study was community based cross sectional design conducted from March 10-Apr 10, 2020, using a multi stage sampling techniques. Structured interview, Depression, Anxiety and Stress Scale (DASS-21), and logistic regression analysis (95% CI, p value < 0.05) was used. This study included 420 respondents who were living in the Gedeo zone. In total, 44.4% of respondents had a psychological problem (21.4% mild and 23% moderate level of the mental crisis). Gender female, monthly income below 500 ETB, and more than three family size were variables associated with the outcome variable (p < 0.05). Nearly half of the respondents had mild to the moderate mental crisis in response to the pandemic. It is better to give mental health support for the peoples living in the zone to enhance their mental resilience.

Combating the Spread of COVID-19 Through Community Participation.

India has a vast population with a weak public health system, which is vulnerable to the COVID-19 pandemic. Economically and physically, India is in a state of considerable risk of the COVID-19 pandemic. Community participation through various measures is the only way to limit the spread of the virus. The present study investigates the possibility of social intervention and involvement in controlling the pandemics and its cascading effect. The study identifies 5 'S', namely, segregation, sensitization, social fencing, solidarity, and social services, to control the disease through people's participation that could throw insights into controlling the virus and minimizing the aftershock of the pandemic.