scholarly journals Sentiment analysis of Twitter posts related to the COVID-19 vaccines

2021 ◽  
Vol 24 (3) ◽  
pp. 1727
Author(s):  
Noralhuda N. Alabid ◽  
Zainab Dalaf Katheeth
Keyword(s):  
Public Health ◽  
Support Vector Machine ◽  
Financial Support ◽  
Support Vector ◽  
Classification Models ◽  
The People ◽  
Framework Model ◽  
The World ◽  
Public Health Institutions ◽  
Health Institutions

A real threat to the people of the world has appeared as a result of the spread of the Coronavirus disease of 2019 (COVID-19) disease. A lot of scientific and financial support has been made to devote vaccines capable of ending this epidemic. However, these vaccines have become a subject of debate between individuals, as some people tend to support taking vaccines and others rejecting them. This paper aims to create a framework model to classify the sentiment and opinions of individuals that published in Twitter regarding the COVID-19 vaccines. Identify those opinions can help public health institutions to know public opinions and direct their efforts towards promoting taking vaccinations. Two of the machines learning classification models which are the support vector machine (SVM) and naive Bayes (NB) classifier are applied here. Other pre-processing methods were applied as well to filter unstructured tweets.

scholarly journals The First Few Hundred Cases for Coronavirus Disease 2019 (COVID-19) in Colombia

2020 ◽  
Author(s):  
Julián Felipe PORRAS-VILLAMIL ◽  
Mario Javier OLIVERA ◽  
Nadia Katherine RÍOS-CAMARGO
Keyword(s):  
Public Health ◽  
Observational Study ◽  
Geographical Distribution ◽  
Causative Agent ◽  
Age Group ◽  
News Reports ◽  
The World ◽  
Public Health Institutions ◽  
Health Institutions ◽  
Epidemiological Characteristics

Background: SARS-CoV-2 virus is the causative agent of COVID-19 disease. It is essential to understand the epidemiological characteristics of the first few cases in each country. This study aimed to describe the geographical distribution, and temporal appearance of the first few hundred cases in Colombia. Methods: This observational study was conducted to review the literature and key documentary information from public health institutions, websites and news reports were examined. Results: The first few 100 cases for COVID-19 were confirmed in Colombia. According to sex, men with 54% predominate, the most affected age group was 20 to 29 yr old (26%), 9% of the cases required hospitalization and no deaths were reported. Most of the confirmed subjects were from the departments of Cundinamarca. To date, most cases are imported (63%), especially from Spain. Conclusion: The COVID-19 pandemic puts in evidence the lack of understanding, prevention and contention power of the different countries around the world is not as good as it could be. Politics must not affect the different proposed measures.

scholarly journals From hostile worlds to multiple spheres: towards a normative pragmatics of justice for the Googlization of health

2021 ◽  
Author(s):  
Tamar Sharon
Keyword(s):  
Public Health ◽  
Market Value ◽  
Theory Of Justice ◽  
Alternative Framework ◽  
Orders Of Worth ◽  
Public Health Institutions ◽  
Health Institutions ◽  
Normative Pragmatics ◽  
The Many ◽  
Critical Perspectives

AbstractThe datafication and digitalization of health and medicine has engendered a proliferation of new collaborations between public health institutions and data corporations like Google, Apple, Microsoft and Amazon. Critical perspectives on these new partnerships tend to frame them as an instance of market transgressions by tech giants into the sphere of health and medicine, in line with a “hostile worlds” doctrine that upholds that the borders between market and non-market spheres should be carefully policed. This article seeks to outline the limitations of this common framing for critically understanding the phenomenon of the Googlization of health. In particular, the mobilization of a diversity of non-market value statements in the justification work carried out by actors involved in the Googlization of health indicates the co-presence of additional worlds or spheres in this context, which are not captured by the market vs. non-market dichotomy. It then advances an alternative framework, based on a multiple-sphere ontology that draws on Boltanski and Thevenot’s orders of worth and Michael Walzer’s theory of justice, which I call a normative pragmatics of justice. This framework addresses both the normative deficit in Boltanski and Thevenot’s work and provides an important emphasis on the empirical workings of justice. Finally, I discuss why this framework is better equipped to identify and to address the many risks raised by the Googlization of health and possibly other dimensions of the digitalization and datafication of society.

COVID-19: implications for NHS Staff

2020 ◽  
Vol 14 (4) ◽  
pp. 193-197
Author(s):  
Alan Glasper
Keyword(s):  
World Health ◽  
The Novel ◽  
Emeritus Professor ◽  
The World ◽  
Clinical Environments ◽  
Public Health Institutions ◽  
Health Institutions ◽  
Health Organization ◽  
The University

In light of the emergence in China of COVID-19, the novel corona virus, emeritus professor Alan Glasper, from the University of Southampton discusses the role of the World Health Organization and other public health institutions in responding to potential new global pandemics and deliberates on the role of NHS staff in coping with infectious disease in clinical environments.

scholarly journals Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

2021 ◽  
Author(s):  
Lance F Merrick ◽  
Dennis N Lozada ◽  
Xianming Chen ◽  
Arron H Carter
Keyword(s):  
Support Vector Machine ◽  
Winter Wheat ◽  
Genomic Selection ◽  
Stripe Rust ◽  
Regression Models ◽  
Prediction Models ◽  
Support Vector ◽  
Classification Models ◽  
Breeding Lines ◽  
Classification And Regression

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT) data in germplasm screening nurseries generally do not follow these assumptions. On this regard, researchers may ignore the lack of normality, transform the phenotypes, use generalized linear models, or use supervised learning algorithms and classification models with no restriction on the distribution of response variables, which are less sensitive when modeling ordinal scores. The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. We extensively compared both regression and classification prediction models using two training populations composed of breeding lines phenotyped in four years (2016-2018, and 2020) and a diversity panel phenotyped in four years (2013-2016). The prediction models used 19,861 genotyping-by-sequencing single-nucleotide polymorphism markers. Overall, square root transformed phenotypes using rrBLUP and support vector machine regression models displayed the highest combination of accuracy and relative efficiency across the regression and classification models. Further, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. This study showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.

scholarly journals Occupational exposure to blood/body fluid splash and its predictors among midwives working in public health institutions at Addis Ababa city Ethiopia, 2020. Institution-based cross-sectional study

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251815
Author(s):  
Solomon Shitu ◽  
Getachew Adugna ◽  
Haimanot Abebe
Keyword(s):  
Public Health ◽  
Health Care Professionals ◽  
Body Fluid ◽  
Infection Prevention ◽  
Addis Ababa ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Public Health Institutions ◽  
Health Institutions

Background Blood/body fluid splash are hazards to health care professionals in their working area. Around twenty bloodborne pathogens are known to be transmitted through these occupational injuries. This problem alters the health status of health care professionals in different ways, including physically, mentally, and psychologically. Even though health professionals especially midwives who are working in delivery rooms are highly affected, little is known about the exposure. So, this study was aimed to assess the prevalence of exposure to blood/body fluid splash and its predictors among midwives working in public health institutions of Addis Ababa city. Methods Institution based cross-sectional study was conducted among 438 study participants in public health institutions in Addis Ababa. Data was collected from March 1–20, 2020 by a self-administered questionnaire. The data were entered into Epi data version 3.1 and then exported to SPSS version 24 for analysis. All variables with P<0.25 in the bivariate analysis were included in a final model and statistical significance was declared at P< 0.05. Results In this study, a total of 424 respondents respond yielding a response rate of 97%. The prevalence of blood and body fluid splashes (BBFs) was 198 (46.7%). Not training on infection prevention, working in two shifts (> 12 hours), not regularly apply universal precautions, job-related stress, an average monthly salary of 5001–8000 were independent predictors of blood and body fluid splashes. Conclusion The study revealed that nearly half of midwives were exposed to BBFS. This highlights the need for key stakeholders such as policymakers and service providers to design appropriate policies to avert this magnitude and making the environment enabling to comply with standard precautions. We recommend that this study may be done by including rural setting institutions and by including other health professionals that are susceptible to BBFS at work. Formal training on infection prevention and safety practice to apply universal precautions will be needed from the concerned bodies to prevent exposures to blood/body fluid splash.

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