Using the SIR Model to Analyze the Epidemiological Situation in Kazakhstan and Neighboring Countries

In this paper, we study the epidemic situation in Kazakhstan and neighboring countries, taking into account territorial features in emergency situations. As you know, the excessive concentration of the population in large cities and the transition to a world without borders created ideal conditions for a global pandemic. The article also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by types of models (basic SIR model, modified SEIR models) and the practical application of the SIR model using an example (Kazakhstan, Russia, Kyrgyzstan, Uzbekistan and other neighboring countries). The obtained processing results are based on statistical data from open sources on the development of the COVID-19 epidemic. The result obtained is a general solution of the SIR-model of the spread of the epidemic according to the fourth-order Runge-Kutta method. The parameters β, γ, which are indicators of infection, recovery, respectively, were calculated using data at the initial phase of the Covid 2019 epidemic. An analysis of anti-epidemic measures in neighboring countries is given. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

Experience with a mobile CT scanner as part of an airmobile hospital of the Ministry of Emergencies of Russia in the field conditions of the Far North to combat the outbreak of a new coronavirus infection.

Background. Large-scale construction of industrial and transport facilities is underway in the Far North of Russia. The process involves more than 10,000 shift workers, and there was a Covid19 outbreak in this population. In order to contain the outbreak and prevent the spread of infection in this area the Russian Emergencies Ministry deployed an airmobile hospital. Purpose. The purpose is to present an experience of work with the mobile CT scanner as part of an airmobile field hospital deployed in the Far North of Russia to combat the Covid-19 outbreak. Materials and methods. On April 6, 2020, the construction site reported a "zero patient" who sought medical aid;the PCR test showed positive results of coronavirus. In the first half of April, over 300 rotation employees applied for medical care, most of them had a positive PCR test. On April 11, a state of emergency was declared in the construction site and, on April 17, 2020, airmobile hospital started operations. Its mission lasted 54 days. The mobile CT scanner (Brightspeed Elite Mobile, GE) was transported by land. The field hospital closely cooperated with the nearest medical institutions and the regional clinical hospital. Results. During its work the airmobile hospital examined 1,678 rotational workers and 408 employees of the Ministry of Emergency Situations of the Murmansk region, with 2,086 CT scans performed. The average age of the patients was 37.8 years, men predominated. In 91.2% of patients, fever was the first symptom of the disease. Blood saturation results ranged from 92% to 99%. The degree of lung involvement ranged from CT 0 to CT 4. During the work of the airmobile hospital, COVID-19 was diagnosed in 500 people, including 328 cases of mild form, 98 - moderate, 74 - severe, no mortalities. Conclusion. A positive experience of application of the mobile CT scanner as part of the AMH field hospital in unfavorable epidemiological conditions of the Far North of the Russian Federation was obtained. CT plays a key role in early detection of infection, differential diagnosis, and identification of complications. Determination of the severity of the disease based on CT data is crucial for patient routing.Copyright © 2021 Medical Visualization. All rights reserved.

Hospital Resource Management -A Survey

Healthcare is a limited resource that is constantly in high demand because everyone requires it. When demand exceeds supply, resources become relatively scarce, making the overall resource allocation in healthcare even more difficult, as we have seen at the time of COVID-19. Effective resource allocation faces obstacles such as a lack of trained human resources, inefficient resource use, a lack of focus on improvement, and inefficient resource reallocation. This paper will outline a study of the numerous approaches to resource allocation in healthcare, outlining the methods employed, the outcomes, and benefits and drawbacks of each approach. In order to address any kind of emergency situation that may arise in the future, it was our goal to pinpoint the research gap between the work that had already been done and the solution to this problem through the survey analysis. In order to boost hospital resource management, the paper identifies a variety of potential solutions which can be categorized further into subcategories which can be seen through different perspectives and a range of approaches that can be implemented during COVID-19 or in any other emergency condition. © 2023 IEEE.

Unlearning in crisis: Forces of change in unlearning

The study of unlearning continues to be important, not only due to the relevance of the concept itself, but in light of current strong, unforeseen forces, knowledge change opportunities have been created beyond our prediction. A knowledge exchange is often needed to revise processes, use new technologies, or due to forces that stem from catastrophic situations. Examples include economic, such as in business failures or the recent public health concerns from the COVID-19 pandemic. Building from new insights using the typological model from Rushmer and Davies (2004), deep unlearning may the end result of catastrophic forces of change. First, deep unlearning occurs with striking events, or yield change that adds anxiety, psychological, or technological upset. Second, inherent in many catastrophic changes are rapid interruptions in the trajectory of "previous” actions and unique processes toward recovery where knowledge base may be forever altered. We address the following question: "Is Rushmer and Davies' deep unlearning typology exhibited during catastrophic situations?” This theoretical paper examines the concept of deep unlearning, the process of replacement or lack of use of a belief, action, or process in a context of an emergency situation where little is currently known. What type of agent for change would be needed? Will unintended consequences not be identified by individuals and organizations;what may be the cost to future learning skills when deep unlearning of current tasks occurs? Third, some insights and directions for future research are presented. © 2022 IEEE Computer Society. All rights reserved.

A Citizen Science Approach for Analyzing Social Media With Crowdsourcing

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations. © 2013 IEEE.

The spatial diffusion mechanism of Health QR Code Policy in the context of COVID-19

As a public health emergency, the COVID-19 has led to a devastating consequence, such as casualties and property losses on a global scale. Since February 2020, in order to prevent the spread of the epidemic as well as to promote the resumption of work and production, governments at all levels across China successively decided to take action and introduce the Health QR (quick response) Code Policy. What is known is that the Health QR Code Policy has become an important means and practice for effectively preventing and controlling the disastrous epidemic in China up to now. Based on the Event History Analysis (EHA) of the diffusion time and influencing factors of 295 cities at and above the prefecture level in China, this paper explores the spatio-temporal process and mechanism of the rapid policy implementation in tackling the pandemic across China, what is worth paying attention to is that the policy was first initiated and adopted by a provincial government. The findings are as follows: (1) The cities with higher digitization and economic strength would have a faster response to adopt the Health QR Code Policy. (2) What is worth considering is that the "learning" and "competition" behaviors among governments of neighboring cities would speed up the diffusion of the Health QR Code Policy, while the vertical guidance pressure of provincial governments did not play a significant role. (3) During the COVID-19, policy entrepreneurs have played a significant role in public emergency and become a powerful force that can accelerate the diffusion of Health QR Code Policy. (4) The epidemic situation of each city would affect the transmission rate of the Health QR Code Policy. There is no doubt that the geographical distance from the epidemic hotspots would also affect the governments to adopt the Health QR Code Policy in a short period. This paper, by analyzing the diffusion motivations of the Health QR Code Policy during the COVID-19 pandemic, could provide a predominant summary of experience and policy suggestions for understanding the formulation of emergency policies as well as the diffusion mechanism in the context of public crisis. © 2022, Science Press. All rights reserved.

The untapped potential of Games for Health in times of crises. A critical reflection.

Given its promising role in public health to address hard to reach population groups, game-based interventions (i.e., Games for Health, G4H) have experienced growing interest in recent years. Therefore, it is surprising that they have played only a minor role during the COVID-19 pandemic. Hence, the aim of this paper is to reflect the opportunities and challenges of G4H especially during the pandemic but also with regard to future health crises. As commercial video games (i.e., those that primarily aim to entertain its users) were often used to deal with the containment measures during the COVID-19 pandemic, we call for greater cooperation with commercial game makers to distribute health-related messages via entertainment games. With regard to G4H we see a need to (i) strengthen the intervention theory underlying game-based applications, (ii) to enhance the appeal of games in order to maintain the interest of users in the long term, and (iii) to improve the evidence base using appropriate study designs. Finally, we argue for (iv) greater user involvement, both in terms of developing game-based approaches and as co-researchers in solving complex health problems.

Impulse purchases during emergency situations: exploring permission marketing and the role of blockchain

Purpose: The paper explores how consumer behavior for purchasing impulse products changed in the complex and disruptive (emergency) situation of the COVID-19 pandemic when the customer is shopping in-home and not visiting the offline stores in an emerging economy context. This paper further explores how digital transformations like the use of blockchain technology can aid offline/omnichannel retailers in reviving sales via permission marketing for impulse products. Design/methodology/approach: The authors followed a qualitative research design and conducted 24 personal interviews with millennials and 15 interviews with offline/omnichannel retailers from an emerging economy. The data collected were analyzed using the thematic analysis procedure. Findings: The authors discuss their findings under three themes – customers' conscious impulse buying during the pandemic, customers' unconscious impulse buying during the pandemic, and a viable solution for retailers in response to the pandemic. Practical implications: The authors suggest that marketers primarily from an offline/omnichannel store should adapt to permission marketing and use technologies like blockchain for the digital transformation of their marketing strategies. Doing so can help offline retailers minimize future damages in the retail sector during emergency situations. Originality/value: This paper is one of the first that explores how impulse – pure, suggestion, planned and reminder – purchases got affected during the COVID-19 pandemic disruptions in an emerging economy. This paper is also one of the first to explore the role of permission marketing and digital transformation by the use of blockchain in helping offline retailers in forming swift trust and practice trust-based marketing. © 2022, Emerald Publishing Limited.

Short-Term State Electricity Load Forecasting Based on Transfer-Informer

The worldwide COVID-19 pandemic has caused an enormous impact on the operation mode of human society. Such sudden events bring sharp fluctuations and data inadequacy in datasets of several areas, which leads to challenges in solving related problems. Traditional deep learning models like CNN have shown relatively poor performance with small datasets during the COVID-19 pandemic. This is because the data insufficiency and fluctuations lead to serious problems in the training process. In our work, an Informer framework combined with Transfer learning methods (Transfer-Informer) is proposed to solve the data insufficiency in emergency situations, as well as to provide a more efficient self-attention mechanism for deep feature mining, with two distinctive advantages: (1) The ProbSpares self-attention mechanisms, which enables the proposed model to highlight dominant information and extract more typical features from time-series datasets. (2) The Transfer learning framework improves the generalization capability of the model, by transferring basic knowledge from normal situations to emergency cases with fewer data. In our experiments, Transfer-Informer is applied to short-term load forecasting, which achieves better predicting accuracy than traditional models. The empirical results indicate that the proposed model has put forward a baseline for short-term load forecasting in emergency situations and provided a feasible method to tackle sudden fluctuations in real problem-solving. © 2022 IEEE.

Short-Term State Electricity Load Forecasting Based on Transfer-Informer

The worldwide COVID-19 pandemic has caused an enormous impact on the operation mode of human society. Such sudden events bring sharp fluctuations and data inadequacy in datasets of several areas, which leads to challenges in solving related problems. Traditional deep learning models like CNN have shown relatively poor performance with small datasets during the COVID-19 pandemic. This is because the data insufficiency and fluctuations lead to serious problems in the training process. In our work, an Informer framework combined with Transfer learning methods (Transfer-Informer) is proposed to solve the data insufficiency in emergency situations, as well as to provide a more efficient self-attention mechanism for deep feature mining, with two distinctive advantages: (1) The ProbSpares self-attention mechanisms, which enables the proposed model to highlight dominant information and extract more typical features from time-series datasets. (2) The Transfer learning framework improves the generalization capability of the model, by transferring basic knowledge from normal situations to emergency cases with fewer data. In our experiments, Transfer-Informer is applied to short-term load forecasting, which achieves better predicting accuracy than traditional models. The empirical results indicate that the proposed model has put forward a baseline for short-term load forecasting in emergency situations and provided a feasible method to tackle sudden fluctuations in real problem-solving. © 2022 IEEE.

Sugammadex in Emergency Situations.

Sugammadex may be required or used in multiple emergency situations. Moderate and high doses of this compound can be used inside and outside the operating room setting. In this communication, recent developments in the use of sugammadex for the immediate reversal of rocuronium-induced neuromuscular blockade were assessed. In emergency surgery and other clinical situations necessitating rapid sequence intubation, the tendency to use rocuronium followed by sugammadex instead of succinylcholine has been increasing. In other emergency situations such as anaphylactic shock caused by rocuronium or if intubation or ventilation is not possible, priority should be given to resuming ventilation maintaining hemodynamic stability, in accordance with the traditional guidelines. If necessary for the purpose of resuming ventilation, reversal of neuromuscular blockade should be done in a timely fashion.

Virtual Reality Environment as a University Online Education Platform

The recent COVID-19 epidemics resulted into much more extensive online distance education at all universities. The growing power of Information and Communication Technologies allowed considering educational approaches unimaginable recently. The return to the previous stage is many university educators' desire. At the same time, no one can exclude similar emergency situations in the future. The universities and their educators have to be better prepared for them. In this paper, we outline a solution: the creation of a 'mirror' image of a real campus in a Virtual Reality Environment. The quotation marks indicate the fact that the VRE should not only replicate the study programs but imitate the university climate. Design and development of such campus is undergoing;the first pilot projects had run and are now evaluated. The paper analyses the key advantages it offers and drawbacks it may bring. © 2022 IEEE.

Education challenges after Covid-19

Covid-19 meant a huge challenge for everybody in the world. From one day to the other home-office became widespread in a lot of professions. Education was one of the areas where we faced a lot of troubles. In 2020 spring semester we had to quickly transform all classical university courses to online ones without having appropriate knowledge about distance teaching methods and applications. During the semesters under emergency situation, we had to learn a lot to enhance our education quality. Students used to these comfortable and modern possibilities and now, we are back in school, and we face a new problem. Is it possible to go back and continue education the same way as we did before Covid-19-can we step in the same river twice? We made a survey just after the starting of distance education and after the first semester back to classical face-to-face teaching to collect the opinions of students. In this paper we should like to present the experienced change in students' attitude which we should adopt into our work. © 2022 IEEE.

Early Usability Evaluation of a Relational Agent for the COVID-19 Pandemic

Relational agents (RAs) have shown effectiveness in various health interventions with and without healthcare professionals (HCPs) and hospital facilities. RAs have not been widely researched in COVID-19 context, although they can give health interventions during the pandemic. Addressing this gap, this work presents an early usability evaluation of a prototypical RA, which is iteratively designed and developed in collaboration with infected patients (n=21) and two groups of HCPs (n=19, n=16) to aid COVID-19 patients at various stages about four main tasks: testing guidance, support during self-isolation, handling emergency situations, and promoting post-infection mental well-being. The prototype obtained an average score of 58.82 on the system usability scale (SUS) after being evaluated by 98 people. This result implies that the suggested design still needs to be improved for greater usability and adoption. © 2022 Owner/Author.

Smart City Energy Technology in the Face of Emergency Situations: Electric Supply, Electric Transportation, and Communication

This article examines critical smart city infrastructure components, like electricity supply, transportation, and telecommunication, in the face of an emergency like COVID-19. The electricity infrastructure is a critical component of any smart city and significantly impacts other systems, like transportation, communication, and water delivery and treatment. © 2022 IEEE.

COMPARATIVE STUDY ON THE UNDERSTANDING OF RESPONSIVENESS IN PRE-SERVICE PRESCHOOL TEACHERS IN THE COVID-19 EMERGENCY SITUATION (2018-2020)

The global pandemic of Civid-19 has led to significant changes in the transformation of the system of education and teacher training. Regarding the training of prospective preschool teachers at the institutions of higher education, there is a growing need to develop multiple competencies so that a teacher can create an emotionally safe and supervised learning environment. Our study highlights the importance of the responsiveness of preschool teachers in providing supportive responses, responsibility, and guidance regulating children's emotions in the pedagogical activity. The aim of the study is to analyze and compare the perceptions of students, pre-service preschool teachers, on teachers' emotional responsiveness before the global pandemic and during the Covid-19 emergency. The materials and methods used in the study include the theoretical method - the analysis of literature - and the empirical method - a survey of 600 part-time students working in pre-school educational institutions in 2018 and in 2020. The survey was conducted online. The data were processed using the software SPSS. The results of the study demonstrate that, generally, the Covid-19 emergency has increased the willingness of prospective preschool teachers to undertake responsibility and leadership in regulating children's emotions. The students have become more responsible and determined;their understanding of the importance of emotional responsiveness in teacher's work is higher than before the pandemic.

Is the Emergency Distance Teaching ‎Experience Different in Postgraduate ‎Programs?: Students' Voices

This study evaluates emergency remote teaching for postgraduate programs. A descriptive-analytic ‎method was used, including quantitative and qualitative tools. A questionnaire (N = 144) was ‎administered based on the context, input, process, and product (CIPP) model for evaluation, and semi-‎structured interviews (N = 6 participants) were conducted to provide a comprehensive depiction ‎incorporating participants’ views from three Saudi universities. The results revealed participants had a ‎positive bias regarding their experience;the results were similar to those of a number of studies but ‎revealed increased consistency of distance learning characteristics, specifically, data, exceptions, and ‎objectives of higher stages. This study also revealed several transitive and positive effects along with ‎challenges that seem to confront not only emergency distance teaching but the whole experience of ‎distance learning.‎

Military & War Metaphor during Covid-19 in India

War and military metaphors are commonly used in the situation where the government requires strong and strict decisions. Metaphors may play a crucial role in the healthcare domain when there is an emergency situation like COVID-19 pandemic. In the era of social media, the emergency communication can be disseminated in most efficient manner. This paper proposes and investigates the use of war and military metaphor in the Twitter and its impact on the citizen and the government machinery. We used data mining tools to extract data from the Twitter and subjected it for sentiment analysis. The metaphor perception in the healthcare context have different dimension. It will induce fear and insecurity in the citizen and more authoritative nature in the people in power. The use of war and military metaphors will have mixed responses depending the socio-economic and cultural background of the person. The war and military metaphor will prevail until the quest for power by the human being exists in nature. The war and military are the having different perceptions among the people lives in fear in the war hit region and the people never experienced the war situation. © 2021 IEEE.

Rescheduling Senior University for a blended teaching-learning model

This article explains how Senior University at the University of A Coruna (NW Spain) was adapted to the blended learning model in the 2020/2021 academic year, as a result of the emergency situation brought about by COVID-19. The reorganization had to take into account the needs of both teaching staff and participants. The students had to overcome obstacles to embrace new technologies and follow what was being taught. The results of the surveys handed out to teachers and students at the start of the year are summarized here. A brief description of the new Specific Training Program “Current Events, Science, Health and Life” is also provided, as well as the outcomes of the study carried out to know to what extent the key players -teaching staff, students and Senior University management team- felt satisfied with the new program. All in all, both teachers and students replied that they were very satisfied with the blended learning model, which will continue running into the following academic years as a complement to face-to-face teaching. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).