Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach

Emotion classification has become a valuable tool in analyzing text and emotions people express in response to events or crises, particularly on social media and other online platforms. The recent news about monkeypox highlighted various emotions individuals felt during the outbreak. People’s opinions and concerns have been very different based on their awareness and understanding of the disease. Although there have been studies on monkeypox, emotion classification related to this virus has not been considered. As a result, this study aims to analyze the emotions individual expressed on social media posts related to the monkeypox disease. Our goal is to provide real-time information and identify critical concerns about the disease. To conduct our analysis, first, we extract and preprocess 800,000 datasets and then use NRCLexicon, a Python library, to predict and measure the emotional significance of each text. Secondly, we develop deep learning models based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and the combination of Convolutional Neural Networks and Long Short-Term Memory (CLSTM) for emotion classification. We use SMOTE (Synthetic Minority Oversampling Technique) and Random Undersampling techniques to address the class imbalance in our training dataset. The results of our study revealed that the CNN model achieved the highest performance with an accuracy of 96%. Overall, emotion classification on the monkeypox dataset can be a powerful tool for improving our understanding of the disease. The findings of this study will help develop effective interventions and improve public health. Author

IOT based Temperature and Oxygen level monitoring and Data visualization

The Covid-19 pandemic that hit us in 2020 changed our lifestyle in every way. There was tremendous damage to people's lives. It is now predicted that other variants of Coronavirus are affecting people's health throughout the world. We must remain vigilant against upcoming dangers. The Indian health ministry has also advised people to take the necessary precautions. In this paper, we will focus on automating temperature and oxygen monitoring using the Internet of Things. According to our proposed model, data generated by the temperature sensor (MLX90614) and oxygen saturation sensor (MAX30102) will be stored in a relational database. Using this data, future data analyses can be conducted. We are also going to visualize the data by building an interactive dashboard using Power BI. Overall, health monitoring will become much more convenient and speedier. © 2023 IEEE.

Hospital admission, facility‐based isolation, and social distancing: An SEIR model with constrained medical resources

COVID‐19 is a highly contagious disease that has spread to most countries at unprecedented transmission speed. Medical resources and treatments provided by the healthcare system help reduce the mortality rate and spread of COVID‐19 by isolating infectious individuals. We introduce a modified SEIR model that considers individuals access to limited medical resources to characterize the central role of medical resources during the pandemic. We discuss how the three hospital admission policies (hierarchy, mixed, and Fangcang healthcare system) affect the spread of the disease and the number of deaths and infections. We find that the Fangcang system results in the least number of infections, deaths, and occupied beds. When hospital capacity is relatively high or the transmission rate of the mildly infected patient is not ignorable, a mixed system can lead to fewer infections and deaths than a hierarchy system, but greater numbers of occupied beds. This occurs by preventing disease transmission to a great extent. The results are confirmed by our surveys with healthcare workers in major hospitals in Wuhan, China. We also investigate the performance of the three healthcare systems under a social distancing policy. We find that the Fangcang system results in the largest reduction in infections and deaths, especially even when the medical capacity is small. Moreover, we compare a one‐time off policy with a bed trigger policy. We find that a one‐time off policy could achieve the similar performance as bed trigger policy when it is initiated neither too early nor too late.

Attitudes Towards COVID-19 Contact Tracing Apps: A Cross-National Survey

To help prevent the spread of COVID-19, countries around the world have implemented a range of measures and virus containment strategies, including digital contact-tracing (DCT) in the form of smartphone apps. While early studies showed a high level of acceptability of such technologies, the adoption rates varied greatly between countries after contact-tracing apps became available to download. This cross-national user survey (n=871) aims to explore public attitudes and factors that affect user acceptability and adoption of contact-tracing apps in the USA, UK, and the Republic of Ireland, which employ similar underlying technology, but have uneven adoption rates. The results indicate interactions between installation decisions and public trust in actors and institutions communicating COVID-related information, and releasing such technologies. Beyond the immediate case of contact tracing, our findings hold implications for the deployment and communicative framing of technology for public health and the public good, and inform the design of crisis response public health information systems.

Think About It: Promoting Physical Activity with a Mobile App with a Theory-based Approach

Currently, people’s highly busy lifestyles and sedentary behavior contribute negatively to multiple health factors. During the COVID-19 pandemic, the different sanitary measures, such as limited mobility and the closing of gyms and sports centers, have contributed to limited physical activity. In this context, there are several apps to enhance physical activity across all mobile stores with an emphasis on mobile sensing. However, the use of a formal theory incorporated into the app development and interventions is less evident. A theory-based approach contributes to understanding the reasons and situations in which an intervention strategy can have an impact. The present work considers the Elaboration Likelihood Model (ELM), which addresses persuasion and attitude change. Can we develop a persuasive app that promotes physical activity based on contemporary attitudes and behavioral change theories? We developed a mobile application for Android OS. Then, 63 participants tested it, and were encouraged to think of ideas or arguments in favor of doing physical activity in a high elaboration task. A mediation analysis was done, with results showing that attitudes partially mediate the association between thought and physical activity. Participants’thoughts were seen to be positively correlated with their attitudes;and, in turn, participants’attitudes were correlated with their behavioral intention (to do physical activity). This suggests that a theory-based approach for the active production of biased beliefs is effective when designing an app that encourages positive attitudes toward physical activity. Author

Human Rights Violations and Mistrust among Refugees in South Africa: Implications for Public Health during the COVID Pandemic

Despite the open policy of integration, refugees in South Africa have been experiencing increasing exclusion and discrimination in socio-economic development and from social services. State-sanctioned discrimination contributes to mistrust among marginalized groups toward the government and its institutions. However, public trust towards healthcare authorities and government institutions is critical during pandemic outbreaks to ensure the population's willingness to follow public health initiatives and protocols to contain the spread of a pandemic. Eleven key informants, including refugee community leaders and refugee-serving NGOs, were virtually interviewed about refugees' access to healthcare in South Africa during the COVID-19 pandemic and the consequences of inconsistent access and discrimination on their trust of public healthcare initiatives. Interviews were analyzed using critical thematic analysis. The results suggest that refugees' access to public healthcare services were perceived as exclusionary and discriminatory. Furthermore, the growing mistrust in institutions and authorities, particularly the healthcare system, and misperceptions of COVID-19 compromised refugees' trust and adherence to public health initiatives. This ultimately exacerbates the vulnerability of the refugee community, as well as the wellbeing of the overall population.

Critical success factors of smart card technology in South African public hospitals

Background: Even though the government has set several admirable targets for raising the standard of healthcare, as highlighted by communities and media reports, public health institutions' services continue to fall short of patients' expectations and basic standards of care. For this reason, the general public has lost faith in the healthcare system. The public healthcare system in South Africa is completely dysfunctional and urgently needs to be transformed to serve the majority of those who use public hospitals. Objectives: The study aimed to improve healthcare for the majority of South Africans by investigating the critical success factors (CSFs) that influence the adoption of smart card technology (SCT) in South African public hospitals. Methods: A thorough review of peer-reviewed literature was conducted to determine potential barriers to adopting SCT. Furthermore, a hybrid model that combines the Health Unified Technology of Acceptance Theory (HUTAUT) model, DeLone and McLean IS success model (D&M) and the diffusion of innovation (DOI) theory will be developed, validated and tested to identify the CSFs adoption of SCT in public hospitals in South Africa. Results: The validated research model has been developed to be adopted by nurses at public hospitals. Conclusion: This research will contribute to the development of a new framework that identifies the CSFs for SCT adoption in South African public hospitals. Contribution: The study's results will make a special contribution to the body of knowledge in the fields of health informatics, particularly e-health.

Optimization Models for Medical Procedures Relocation.

As a side-effect of the Covid-19 pandemic, significant decreases in medical procedures for noncommunicable diseases have been observed. This calls for a decision support assisting in the analysis of opportunities to relocate procedures among hospitals in an efficient or, preferably, optimal manner. In the current paper we formulate corresponding decision problems and develop linear (mixed integer) programming models for them. Since solving mixed integer programming problems is NP-complete, we verify experimentally their usefulness using real-world data about urological procedures. We show that even for large models, with millions of variables, the problems' instances are solved in perfectly acceptable time.

Interdisciplinary approach to revealing cardiac pathology in TB patients during the COVID-19 pandemic

The tuberculosis (TB) dispensary patients are often diagnosed with cardiac diseases, but the regulatory documents associated with TB care refer to the procedures and standards of examination and treatment of TB patients or patients with suspected TB while the management of associated chronic diseases is carried out in other medical institutions. There is lack of continuity between doctors of different specialties. Patients face organizational problems of accessibility and quality of medical care. The creation of Department of Medical and Social Care and Rehabilitation in TB dispensary with therapeutic profile specialists became especially urgent in the period of coronavirus pandemic. Aim. The aim of the study was to estimate the expediency and effectiveness of Department of Medical and Social Care and Rehabilitation in detection and treatment of comorbid cardiac pathology in patients of TB dispensary during coronavirus (COVID-19) pandemic. Material and Methods. All patients of the St. Petersburg TB dispensary No. 5 were examined according to specially developed algorithm to detect and treat the cardiac pathology comorbid with TB. Results. Cardiac diseases comorbid with tuberculosis were detected using an integrated multidisciplinary approach to examination of TB patients during the pandemic of coronavirus infection. Examination, consultation, and necessary treatment were carried out directly in TB dispensary allowing to avoid disease exacerbations and complications during the entire period of monitoring. Conclusion. The establishment of Department of Medical and Social Care and Rehabilitation in TB dispensary No. 5 showed the relevance and timeliness of this measure. The coronavirus genesis of some cardiac diseases required to revise the management tactics of affected tuberculosis patients. © 2022 The authors.

Assessing the nexus between COVID-19 pandemic-driven economic crisis and economic policy: lesson learned and challenges.

This study examines China's budgetary policy during the COVID-19 pandemic as a result of China's insufficient ability to deal with a new crisis when the epidemic struck in March 2020 and as a result of the economic crisis that began in China in March 2020. In order to better comprehend China's economic status during COVID-19, the study relies on secondary data. The fiscal response of emerging market economies like India is less than in advanced economies. However, it is generally considered to be in line with the average for emerging market economies. As a result of the Disaster Management authority imposing a rigorous lockdown, unemployment rose, the trade cycle was interrupted, and manufacturing and service activities were affected. According to the study's findings, China's economic policies, namely its fiscal policy, responded in the years leading up to 2019 by increasing health expenditure, income transfer, welfare payments, subsidies, and reducing short-term unemployment. As a result of the COVID-19 pandemic, China's government has adopted a number of measures to minimize the damage to the economy. This article also focuses on China's numerous budgetary actions with COVID-19.

Factors Affecting Access to Public Healthcare Facilities in the City of Tshwane, South Africa.

Access to healthcare services is largely determined by socioeconomic factors, with economically well-off individuals obtaining healthcare services more efficiently than those who are disadvantaged. This paper aims to assess the effects of socioeconomic and other related factors on access to healthcare facilities in the City of Tshwane, South Africa, during the COVID-19 pandemic. Data were sourced from the Gauteng City-Region Observatory (GCRO) quality of life survey (2020/2021). Multivariate logistic regression was applied. Results showed that 66.3% of the respondents reported that they had access to public healthcare facilities within their area. Furthermore, results showed that those who lived in informal houses were significantly (OR = 0.55, 95% CI [0.37-0.80], p < 0.01) less likely to report that they had access to public healthcare facilities in their area compared to those who lived in formal houses. More efforts need to be undertaken to ensure that all citizens have access to public healthcare facilities, especially among those who are disadvantaged, such as informal dwellers. In addition, future research should encompass locality in relation to the factors that affect access to public healthcare facilities, especially during pandemics such as the COVID-19 pandemic, in order to have geographically targeted interventions.

When Privacy, Distrust, and Misinformation Cause Worry About Using COVID-19 Contact-Tracing Apps

Public health authorities worldwide are advocating for contact-tracing apps to help track COVID-19 infections during the pandemic and interrupt virus transmission. However, app users have to share their personally identifiable information, whereabouts, and in some cases, their vaccination records with authorities via mobile Internet. This situation creates grave concerns about how such personal information is transmitted, stored, archived, and disposed. In addition, the apps'technical design would also impact the adoption, such as whether the apps would drain the battery. Further, citizens'high distrust of governments also reduces app adoption. This article reviews recent research on contact-tracing apps and examines how privacy concerns, distrust in governments, and misinformation affect people's perceptions of contact-tracing apps. We recommend possible solutions for promoting these apps by analyzing what we learn from recent literature. IEEE

Mechanism of competitiveness management in the public healthcare: a practical aspect

Healthcare is a key element of the state's national security. The effectiveness of the healthcare industry functioning is a relevant object of research today, especially after the outbreak of the COVID-19 pandemic, during which this sector globally demonstrated its potential and problems. It necessitates a comprehensive assessment of the competitiveness of the healthcare industry and institutions as an important component of their effective management. The objects of the research are public healthcare and institutions as service organizations. The purpose is to develop a scientific-methodical approach to managing the competitiveness in the country's healthcare industry. That is, the research is aimed at developing a market-oriented competitiveness management mechanism for public healthcare institutions. The construction of the mechanism is based on the analysis of statistical data on Ukrainian healthcare institutions according to indicators based on the "7P" marketing concept. Clustering methods, an integral indicator of competitiveness, and multidimensional methods of taxonomic analysis were also used for assessing the level of competitiveness of healthcare institutions and the comparative effectiveness of alternative measures to increase it. © 2022, Bucharest University of Economic Studies Publishing House. All rights reserved.

Geo-Spatial Disease Clustering for Public Health Decision Making

An explosion of interest has been observed in disease mapping with the developments in advanced spatial statistics, data visualization and geographic information system (GIS) technologies. This technique is known as "Geo-Spatial Disease Clustering," mainly used for visualization and future disease expansion prediction. Its importance has been overwhelmingly observed since the COVID-19 pandemic outbreak. Government, Medical Institutes, and other medical practices gather large amounts of data from surveys and other sources. This data is in the form of notes, databases, spread sheets and text data files. Mostly this information is in the form of feedback from different groups like age group, gender, provider (doctors), region, etc. Incorporating such heterogeneous nature of data is quite challenging task. In this regard, variety of techniques and algorithms have been proposed in the literature, but their effectiveness varies due to data types, volume, format and structure of data and disease of interest. Mostly, the techniques are confined to a specific data type. To overcome this issue, in this research, a data visualization technique combined with data warehousing and GIS for disease mapping is proposed. This includes data cleansing, data fusion, data dimensioning, analysis, visualization, and prediction. Motivation behind this research is to create awareness about the disease for the guidance of patients, healthcare providers and government bodies. By this, we can extract information that describes the association of disease with respect to age, gender, and location. Moreover, the temporal analysis helps earlier prediction and identification of disease, to be care of and necessary avoiding arrangements can be taken.

Bridging the Access Gap: The Telepractice Experience of Speech Therapists and Audiologists at a Public Health Care Facility in South Africa

South Africa is a low to middle income country (LMIC) with a population of 60 million people. The public health sector serves more than 80% of the population. Chris Hani Baragwanath Academic Hospital is a central level public health care facility situated in Gauteng. The Speech Therapy and Audiology Department provides insight into their telepractice services through a qualitative approach. The onset of the COVID-19 pandemic resulted in therapists exploring telepractice as a sustainable model of service delivery. Therapists and patients encountered many challenges to the implementation of telepractice, however, the commitment of therapists ensured that creative solutions were developed. A comprehensive needs analysis at public health institutions is required to ensure the sustainability of telepractice. A hybrid model (telepractice and in-person consults) holds the potential to reduce the financial burden on patients and increase access to quality patient-centered care.

Student nurse experiences in public healthcare clinical practice during the COVID-19 pandemic: A qualitative study.

BACKGROUND: The social crises induced by the coronavirus disease (COVID-19) pandemic have had adverse effects on the global healthcare system. Public healthcare centers have restructured their services to address clinical needs at the forefront of the pandemic. This led to nursing students immersing themselves in clinical experience while practicing at public healthcare centers during the COVID-19 pandemic. OBJECTIVES: The aim of this study was to explore and understand the experiences of nursing students practicing in public healthcare centers in response to the COVID-19 pandemic. DESIGN: A qualitative study. SETTING: A public healthcare center in Korea. PARTICIPANTS: Twenty third-year nursing students from a university in Korea. METHODS: A qualitative study design was used where students provided reflections on their experience in clinical practice at public healthcare centers. Data were collected between November 5 and December 17, 2021. Data were analyzed using the phenomenological approach proposed by Colaizzi. RESULTS: Three categories emerged. Students immersed themselves in a practicum experience in public healthcare in response to COVID-19. Students recognized the challenges associated with providing healthcare services during the pandemic. The students expanded their field of interest to community nursing. CONCLUSION: In the context of COVID-19, supplementing academic education and training for nursing students with experience in clinical practice at public healthcare centers enhanced the capabilities of future nurses. It increased confidence in their work and responsibilities.

Using Data Mining to Estimate Patterns of Contagion-Risk Interactions in an Intercity Public Road Transport System

The COVID-19 pandemic has had very negative effects on public transport systems. These effects have compromised the role they should play as enablers of social equity and environmentally sustainable mobility and have caused serious economic losses for public transport operators. For this reason, in the context of pandemics, meaningful epidemiological information gathered in the specific framework of these systems is of great interest. This article presents the findings of an investigation into the risk of transmission of a respiratory infectious disease in an intercity road transport system that carries millions of passengers annually. To achieve this objective, a data mining methodology was used to generate the data required to ascertain the level of risk. Using this methodology, the occupancy of vehicle seats by passengers was simulated using two different strategies. The first is an empirical approach to the behaviour of passengers when occupying a free seat and the second attempts to minimise the risk of contagion. For each of these strategies, the interactions with risk of infection between passengers were estimated, the patterns of these interactions on the different routes of the transport system were obtained using k-means clustering technique, and the impact of the strategies was analysed.

Predictive Factors of the Burnout Syndrome Occurrence in the Healthcare Workers During the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic is probably the most critical epidemiological situation that human civilization has faced in the last few decades. In this context, of all the professional categories involved in the management of patients with COVID-19 are the most likely to develop burnout syndrome. The main objective of this study is to analyze specific predictive factors of the occurrence and development of the burnout syndrome in the healthcare workers involved in the diagnosis and treatment of patients with COVID-19. The study focused on determining factors of the occurrence, development and maintaining the specific burnout syndrome related to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic infection. The study was conducted on a sample of 959 participants, medical personnel from all the public medical entities in Romania(including 5 hospitals): 122 male and 755 female (82 participants did not declare their gender), with a mean age of 42.29 years (SD = 9.97). The sample included 219 doctors, 477 nurses, 214 auxiliary medical personnel and 49 other types of hospital workers. A cross-sectional design was used. Three predictors of the burnout syndrome were identified: Work conditions, Fear of the consequences (including death) determined by the COVID-19 and Need for emotional support. Meaning of work had a moderating role. Several moderated mediation models were tested. The indirect relationship of Work conditions with burnout via Fear of infection was statistically significant; in addition, the indirect effect of Work conditions on burnout through both fear of infection and need for support was statistically significant. The moderation analysis showed that Meaning of work buffer the relationship between Work conditions and Fear of infection. The variance explained by the model including the moderator (30%) was higher than the variance explained by Model 1 (27%), showing that adding the moderating effect of Meaning of work to the relationship of Work conditions with burnout was relevant. The results could be used to design specific interventions to reduce the occurrence of the burnout syndrome in healthcare workers, the implementation of a strategy to motivate employees by highlighting and recognizing the high significance of the work of those in the frontline of the fight against COVID-19.

The use of digital technologies in the response to SARS-2 CoV2-19 in the public health sector

In order to promote public health response to COVID-19, digital technologies are being used around the world. These include population surveillance, case identification, contact tracing, and intervention assessment based on mobility data and public communication. These rapid responses are made possible by the millions of mobile phones in use, massive online data sets, connected devices, low-cost computer resources and machines, and advances in natural language processing. To achieve this goal, a comprehensive review of digital innovations for COVID-19 response to public health around the world is being conducted, including a look at their limitations and implementation obstacles such as legal or ethical issues, privacy concerns, and organizational and personnel issues. We investigate the need for international strategies to improve pandemic control and future preparedness for COVID-19 and other infectious diseases through the regulation, assessment, and use of digital technologies, as well as the need for international strategies to regulate, assess, and use digital technology in pandemic management. © 2022 Elsevier Inc. All rights reserved.

Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two U.S. States

The abundance of available information on social networks can provide invaluable insights into people’s responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social networks, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter-and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users inWashington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets inWashington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts’messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweets that requested readers take action, such as hand washing. Author