Knowledge, Attitude and Practice towards COVID-19 prevention among outpatients from a Private Dental Institution - A Pilot study

In order to slow the COVID-19 pandemic's rapid spread and put an end to it, the world needs to take extraordinary action. The knowledge, attitude, and practises (KAP) of outpatients concerning COVID-19 have an impact on the adherence to control measures. As a result, this research serves as a baseline analysis to assess Knowledge, Attitude, and Practice and serve as the foundation for our mitigation efforts. The outpatients were given this self-administrated survey. The ten-item survey was created in a way that allowed for an accurate evaluation of the knowledge, attitude, and practise components. Using SPSS software, the statistical analysis was conducted. The replies from the Google sheet were loaded into SPSS after being exported to Excel. Data were described using frequency and percentages, and chi square analysis was conducted to see whether there was any correlation between the variables. 85 outpatients in total took part in the survey. While 80% of the participants were aware of the life trajectory of Covid-infected individuals and 77.6% of them paid close attention to government directives, the overall level of awareness about COVID-19 and its prevention was rather high. 54.12% of the participants used hand sanitizer and wore masks constantly. The outcomes indicated that the participants had sound knowledge and a positive outlook. To combat this epidemic, media propaganda and instructional video production must continue to be produced and distributed. © 2023 IEEE.

An actuarial model of the economic cost of the first year of a pandemic and the community mobility index

The COVID-19 pandemic significantly affected world economics. Thus, to anticipate the possibility of a future pandemic, it is crucial to find a proper way to simulate and estimate the cost of a pandemic, which is critical to the economy and welfare. This paper presents an actuarial Susceptible-Infected-Recovered and Death (SIR-D) multiple-state model that estimates the cost of a pandemic through the Cost-of-Illness (COI) analysis for both individual and regional levels. The model can be used to design financial products anticipating future pandemics. Formulas are constructed for two categories of COI, i.e., direct costs and productivity losses. The COIs are calculated annually and weekly throughout the year 2020. We also build and analyze multiple regression models that picture the relationships between community mobility and the amount of economic burden. We apply the model to studying the USA, India, Indonesia, Canada, Australia, and Taiwan. Indonesia, India, and the USA have the world-largest populations. In addition, Australia and Taiwan were known to apply strict border control, tracking, and quarantine in 2020. The models indicate moderate to high correlations between community mobility and economic burden during the first year of the COVID-19 pandemic. © 2023, ICIC International. All rights reserved.

A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports

The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily "world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates. © 2023, The Author(s).

Development of Data Logger for COVID Vaccine Temperature Monitoring System

The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.

Google Classroom as an Alternative during the Covid-19 Pandemic: Teachers Perceptions

Currently, Information and Communication Technologies (ICTs) offer wide access to various educational tools that complement both classroom and online education;therefore, it is the responsibility of institutions to be at the forefront and provide teachers with the necessary technological tools to generate better conditions for meaningful learning. In this sense, the need arises to analyze the use of ICTs during the COVID-19 pandemic as a complement for the teacher in the teaching-learning process. The objective of this research was to analyze the use of Google Classroom during the period March 2020 to February 2021, a tool that supported teachers in communicating with their students at the Universidad Popular de la Chontalpa. For this purpose, a survey was applied to know the perception of 150 teachers of the Economic-Administrative Sciences Division (DCEA) towards the use of the platform. The data analysis was carried out through graphs and their respective description where it was found that most of the teachers accessed the platform to create online classes with students, although their level of knowledge was basic, this software allowed them to maintain interaction with students during the period of suspension of face-to-face classes due to COVID-19;an attitude of acceptance towards its use by teachers and students was perceived. Finally, recommendations were made to continue with teacher training, at intermediate and advanced levels, on the operation and management of the Google Classroom platform, as well as the creation of an area to provide advice to teachers and students in relation to ICTs at the Universidad Popular de la Chontalpa.

Automated Contactless Attendance System

Attendance marking in a classroom is a tedious and time-consuming task. Due to a large number of students present, there is always a possibility of proxy. In recent times, the task of automatic attendance marking has been extensively addressed via the use of fingerprint-based biometric systems, radio frequency identification tags, etc. However, these RFID systems lack the factor of dependability and due to COVID-19 use of fingerprint-based systems is not advisable. Instead of using these conventional methods, this paper presents an automated contactless attendance system that employs facial recognition to record student attendance and a gesture sensor to activate the camera when needed, thereby consuming minimal power. The resultant data is subsequently stored in Google Spreadsheets, and the reports can be viewed on the webpage. Thus, this work intends to make the attendance marking process contactless, efficient and simple. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The interest in online museum experiences and the influence of uncertainty and sentiment factors on tourist arrivals: The case of EU Mediterranean countries

This study examines the determinants of tourist arrivals at hotels and short-stay accommodations for nine EU countries from January 2010 to March 2022. We identify four driving channels of foreign and domestic tourism flows: a traditional, a sentiment, a technological and a health channel. The latter comprises two novel variables: the museum search interest and the infectious disease equity market volatility tracker. The results reveal that traditional and new drivers related to market sentiments and interest in online tourism experiences affect arrivals. Notably, there is a substitution effect between online and in-presence tourism, and the larger the uncertainty, the more substantial the reduction in tourist arrivals. COVID-19 has affected especially Spain and Italy and more foreign than domestic tourists. © 2023 The Authors

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.

Introduction: Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods: The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results: Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion: Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure.

This paper investigates the relationship between investors' attention, as measured by Google search queries, and equity implied volatility during the COVID-19 outbreak. Recent studies show that search investors' behavior data is an extremely abundant repository of predictive data, and investor-limited attention increases when the uncertainty level is high. Our study using data from thirteen countries across the globe during the first wave of the COVID-19 pandemic (January-April 2020) examines whether the search "topic and terms" for the pandemic affect market participants' expectations about future realized volatility. With the panic and uncertainty about COVID-19, our empirical findings show that increased internet searches during the pandemic caused the information to flow into the financial markets at a faster rate and thus resulting in higher implied volatility directly and via the stock return-risk relation. More specifically for the latter, the leverage effect in the VIX becomes stronger as Google search queries intensify. Both the direct and indirect effects on implied volatility, highlight a risk-aversion channel that operates during the pandemic. We also find that these effects are stronger in Europe than in the rest of the world. Moreover, in a panel vector autoregression framework, we show that a positive shock on stock returns may soothe COVID-related Google searches in Europe. Our findings suggest that Google-based attention to COVID-19 leads to elevated risk aversion in stock markets.

Coronavirus-Related Searches on the Internet Predict COVID-19 Vaccination Rates in the Real World: A Behavioral Immune System Perspective.

According to the smoke detector and functional flexibility principles of human behavioral immune system (BIS), the exposure to COVID-19 cues could motivate vaccine uptake. Using the tool of Google Trends, we tested that coronavirus-related searches-which assessed natural exposure to COVID-19 cues-would positively predict actual vaccination rates. As expected, coronavirus-related searches positively and significantly predicted vaccination rates in the United States (Study 1a) and across the globe (Study 2a) after accounting for a range of covariates. The stationary time series analyses with covariates and autocorrelation structure of the dependent variable confirmed that more coronavirus-related searches compared with last week indicated increases in vaccination rates compared with last week in the United States (Study 1b) and across the globe (Study 2b). With real-time web search data, psychological scientists could test their research questions in real-life settings and at a large scale to expand the ecological validity and generalizability of the findings.

Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study.

BACKGROUND: Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, use for this purpose has been limited. OBJECTIVE: The purpose of this study is to determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. METHODS: Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords "chikungunya," "Ebola," "H1N1," "MERS," "SARS," and "Zika," 6 highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these 6 terms, unpaired 2-tailed t tests were used to compare the 2020 weekly scores for each term to their average level over the 3-year study period. The number of Reddit comments per month with each of these 6 terms was collected and then adjusted for the total estimated Reddit monthly comment volume to derive a measure of relative use, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term's pre-COVID monthly comment data, again using unpaired 2-tailed t tests. P value cutoffs for statistical significance were determined a priori with a Bonferroni correction. RESULTS: Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, peaking in March 2020. Google searches for "SARS" and "MERS" remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words "Ebola," "H1N1," "MERS," and "SARS" (P<.001, for each weekly or monthly comparison, respectively). CONCLUSIONS: Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health-related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for 6 distinct infectious causes of epidemics over the last 2 decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider using Google Trends and social media data to identify patterns of engagement with public health topics in real time and to optimize the timing of public health campaigns.

Which COVID-19 information really impacts stock markets?

Information about the COVID-19 pandemic abounds, but which COVID-19 data actually impacts stock prices? We investigate which measures of COVID-19 matter most by applying elastic net regression for measure selection using a sample of the 35 largest stock markets. Out of 24 measures, COVID-19 related Google search trends, the stringency of government responses and media hype prevail during the height of the COVID-19 crisis. These measures proxy for COVID-19 related uncertainty, the economic impact of lockdowns and panic-driven media attention, respectively, summarizing key aspects of COVID-19 that move stock markets. Moreover, geographical proximity to the virus's outbreak and a country's development level also matter in terms of impact.

Googling in Russian Abroad: How Kremlin-Affiliated Websites Contribute to the Visibility of COVID-19 Conspiracy Theories in Search Results

Research that audited search algorithms typically deployed queries in one language fielded from within only one country. In contrast, this study scrutinized 8,800 Google results retrieved in November 2020 from 5 countries (Russia, the United States, Germany, Ukraine, and Belarus) in response to queries on COVID-19 conspiracy theories in Russian and English. We found that the pandemic appeared similar to people who googled in Russian independent of their geolocation. The only exception was Ukraine, which had implemented rigorous media policies to limit the reach of websites affiliated with Russia within its national public sphere. Conspiracy narratives varied with input language. In response to Russian-language queries, 35.5% of the conspiratorial results suspected U.S. plotters to be behind the pandemic (English language: 5.8%). All source pages that blamed U.S. plotters showed connections with Russia's elites. These findings raise important theoretical questions for today's multilingual societies, where the practice of searching in nonlocal languages is increasing.

Selection of Digital Tools for Organizing Students' Group Work in Distance Education

The transformation of classical education into distance education has become increasingly relevant, especially given the challenging conditions posed by the COVID-19 pandemic and military operations in our country. In these circumstances, distance learning requires practical educational materials that are easily accessible to students. This paper proposes criteria and indicators for selecting digital tools that can facilitate group work with educational content in distance learning. We analyzed the tools available on the Microsoft 365, Google Workspace, and Cisco Webex platforms and evaluated their suitability for managing students' group work in distance learning in institutions of higher education at various stages of the learning process including setting tasks, designing the learning environment, facilitating group interaction and communication, managing educational content and files, promoting teamwork and productivity, and presenting results. To obtain expert assessment of the defined criteria and indicators, we engaged seventeen researchers and educators who have practical experience using digital tools in distance learning. Our study identified three important criteria for selecting digital tools for group work with educational content, namely: design, functional-technological, education -communication ones. We determined the weight of the indicators for these criteria and assessed digital tools for group work with educational content in distance learning. Digital tools such as Microsoft 365 and Google Workspace was found to be more suitable for providing group work of students in a distance learning, while Cisco Webex was found to be the most suitable tool for organizing real-time group work. Other digital tools for group work of students in distance learning can be evaluated according to defined criteria and indicators.

Creating Flexibility for Adapted Physical Education Teachers in Supporting Children with Disabilities Using Google Sites and Google Forms

The COVID-19 pandemic has changed education in how learning occurs such as utilizing technological supports. Specific to adapted physical education (APE), the pandemic affected APE teachers' abilities to support their students' physical activity (PA) needs, which produced barriers but also successes in the form of collaboration among APE professionals and increased knowledge, confidence, and use of technology. However, a question remains on what lasting impacts the COVID-19 pandemic will have on education and APE. As such, APE teachers should be prepared to support students in any educational setting and utilize the available resources regarding technology. Additionally, they should have the opportunity to explore different technology approaches to develop competence and determine what aspects can support their students and themselves. Therefore, the purpose of this article is to provide information about two technology resources-Google Sites and Google Forms-that APE teachers can use to support their teaching responsibilities and students. Information about how to utilize both resources are provided as well as suggestions for future use.

Oil in the age of coronavirus: Hysteria or appropriate fall?

We look at the oil price fall in the beginning of 2020 and the effects of coronavirus and the attention towards it on these prices. Such a fall was observed at multiple markets simultaneously with the spread of coronavirus and the panic around it, and oil market wasn't an exception. Using OLS time series models, we investigate - what was the main reason behind such a fall - the coronavirus pandemic itself or rather the attention towards it. We prove the absence of straight effects of the COVID-19 itself on oil prices. At the same time we find significant negative impact of the attention towards COVID-19 on the Internet search on the oil prices. We investigate the role of the OPEC in mitigating the negative impact of coronavirus and the attention towards it. We found that after the OPEC summit both the number of Covid cases and the attention towards the disease lost its influence on oil prices. Our paper is relevant for the behavioral finance researchers, as well as for those who look at the influence of informational shocks on different markets and particularly, on the oil market and at the effect of the COVID-19 on the economy.

The lockdown and vaccination distribution in Thailand's COVID-19 epidemic: A model study.

Background: Several countries used varied degrees of social isolation measures in response to the COVID-19 outbreak. In 2021, the lockdown in Thailand began on July 20 and lasted for the following six weeks. The lockdown has extremely detrimental effects on the economy and society, even though it may reduce the number of COVID-19 instances. Our goals are to assess the impact of the lockdown policy, the commencement time of lockdown, and the vaccination rate on the number of COVID-19 cases in Thailand in 2021. Methods: We modeled the dynamics of COVID-19 in Thailand throughout 2021 using the SEIR model. The Google Mobility Index, vaccine distribution rate, and lockdown were added to the model. The Google Mobility Index represents the movement of individuals during a pandemic and shows how people react to lockdown. The model also examines the effect of vaccination rate on the incidence of COVID-19. Results: The modeling approach demonstrates that a 6-week lockdown decreases the incidence number of COVID-19 by approximately 15.49-18.17%, depending on the timing of the lockdown compared to a non-lockdown scenario. An increasing vaccination rate potentially reduce the incidence number of COVID-19 by 5.12-18.35% without launching a lockdown. Conclusion: Lockdowns can be an effective method to slow down the spread of COVID-19 when the vaccination program is not fully functional. When the vaccines are easily accessible on a large scale, the lockdown may terminated.

Different platforms, different plots? The Kremlin-controlled search engine Yandex as a resource for Russia's informational influence in Belarus during the COVID-19 pandemic

Extant research demonstrated that the algorithms of the Kremlin-controlled search engine Yandex, compared to those of its US-based counterpart Google, frequently produce results that are biased toward the interests of Russia's ruling elites. Prior research, however, audited Yandex's algorithms largely within Russia. In contrast, this study is the first to assess the role of Yandex's web search algorithms as a resource for Russia's informational influence abroad. To do so, we conduct a comparative algorithm audit of Google and Yandex in Belarus, examining the visibility and narratives of COVID-19-related conspiracy theories in their search results. By manually analysing the content of 1320 search results collected in mid-April to mid-May 2020, we find that, compared with Google, (1) Yandex retrieves significantly more conspiratorial content (2) that close to exclusively suspects US plotters to be behind the pandemic, even though the virus spread from the Chinese city of Wuhan across the globe.

Home-based workouts in the era of COVID-19 pandemic: the influence of Fitness YouTubers' attributes on intentions to exercise

Purpose: Home-based workouts via fitness YouTube channels have become more popular during the pandemic era. However, few studies have examined the role of social media personae related to intention to exercise. The purpose of this study was to investigate the structural relationships between fitness YouTuber attributes: perceived physical attractiveness (PPA), perceived social attractiveness (PSA), perceived similarity (PS), parasocial relationships (PSRs), wishful identification (WI), physical outcome expectations (POEs), and continuous intention to work out with fitness YouTubers (CIWFY). Design/methodology/approach: This study considered fitness YouTube channel viewers as the unit of analysis. An online survey was conducted to empirically develop and test the research model using structural equation modeling (SEM). Findings: The SEM empirical findings revealed that the PSRs were significantly influenced by PSA, PPA, and PS. Also, WI was significantly affected by PPA and PS. Furthermore, POEs were significantly impacted by PPA and PSRs. POEs affected the CIWFY. Lastly, PSRs and POEs mediated the influence of PSA and PPA on the CIWFY. Originality/value: The psychological impacts of exercising to online fitness videos in the era of COVID-19, with its untact (no contact) social norms is timely. The study model demonstrated the fitness YouTube viewers' cognitive path from perceptions toward fitness YouTubers' attributes to behavioral intention. To be specific, the current study demonstrated how three attribution types (i.e. PPA, PSA, and PS) of fitness YouTubers affect viewers' PSRs and WI with fitness YouTubers, along with POEs and CIWFY. Along with health practitioners, fitness YouTubers who want to captivate viewers on their channels might need to consider self-attributes from their viewers' standpoint and should build psychological bonding with viewers. [ FROM AUTHOR] Copyright of Internet Research is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier. © 2023 IEEE.