"A lo largo de la historia de la República hemos confrontado numerosas pandemias producidas por distintas enfermedades como la viruela, la peste, el cólera, la influenza, el VIH-Sida y la covid-19. Las respuestas sociales a las pandemias estuvieron marcadas por instancias de pánico, discriminación, y búsqueda de culpables, aunque también por muestras de solidaridad hacia las poblaciones más vulnerables. Las respuestas estatales combinaron el aislamiento de las personas contagiadas, la imposición de cuarentenas, mejoras en las condiciones de la higiene pública y campañas de vacunación. Este libro trata los temas que las acompañan, tan relevantes para la actualidad." -- Cubierta posterior.

Terminando la segunda década del siglo XXI, el mundo se encuentra trágicamente zarandeado por una pandemia, algo que los ciudadanos comunes pensábamos que era cosa del pasado, que la arrogante especie dominante en la Tierra tenía las herramientas para combatir bacterias y virus, o para mantenerlas a raya en un corto período de tiempo, y que el status quo no sufriría cambios repentinos. Por lo que, con alegría o pesar, según el lado de la balanza en la que cada uno se encontraba, la vida transcurría bajo las reglas de una globalización que acentuaba las premisas del capitalismo, tras quedarse sin oponente político después de la caída del muro de Berlín.

The coronavirus disease 2019 (COVID-19) pandemic has caused a global health crisis. Consequently, many countries have adopted restrictive measures that caused a substantial change in society. Within this framework, it is reasonable to suppose that a sentiment of societal discontent, defined as generalized concern about the precarious state of society, has arisen. Literature shows that collectively experienced situations can motivate people to help each other. Since societal discontent is conceptualized as a collective phenomenon, we argue that it could influence intention to help others, particularly those who suffer from coronavirus. Thus, in the present study, we aimed (a) to explore the relationship between societal discontent and intention to help at the individual level and (b) to investigate a possible moderating effect of societal discontent at the country level on this relationship. To fulfil our purposes, we used data collected in 42 countries (N = 61,734) from the PsyCorona Survey, a cross-national longitudinal study. Results of multilevel analysis showed that, when societal discontent is experienced by the entire community, individuals dissatisfied with society are more prone to help others. Testing the model with longitudinal data (N = 3,817) confirmed our results. Implications for those findings are discussed in relation to crisis management. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement. / New York University Abu Dhabi

Background The World Health Organization's "Coordinated Global Research Roadmap: 2019 Novel Coronavirus"outlined the need for research that focuses on the impact of COVID-19 on pregnant women and children. More than one year after the first reported case significant knowledge gaps remain, highlighting the need for a coordinated approach. To address this need, the Maternal, Newborn and Child Health Working Group (MNCH WG) of the COVID- 19 Clinical Research Coalition conducted an international survey to identify global research priorities for COVID-19 in maternal, reproductive and child health. Method This project was undertaken using a modified Delphi method. An electronic questionnaire was disseminated to clinicians and researchers in three different languages (English, French and Spanish) via MNCH WG affiliated networks. Respondents were asked to select the five most urgent research priorities among a list of 17 identified by the MNCH WG. Analysis of questionnaire data was undertaken to identify key similarities and differences among respondents according to questionnaire language, location and specialty. Following elimination of the seven lowest ranking priorities, the questionnaire was recirculated to the original pool of respondents. Thematic analysis of final questionnaire data was undertaken by the MNCH WG from which four priority research themes emerged. Results Questionnaire 1 was completed by 225 respondents from 29 countries. Questionnaire 2 was returned by 49 respondents. The four priority research themes which emerged from the analysis were 1) access to healthcare during the COVID-19 pandemic, 2) the direct and 3) indirect effects of COVID-19 on pregnant and breastfeeding women and children and 4) the transmission of COVID-19 and protection from infection. Conclusion The results of these questionnaires indicated a high level of concordance among continents and specialties regarding priority research themes. This prioritized list of research uncertainties, developed to specifically highlight the most urgent clinical needs as perceived by healthcare professionals and researchers, could help funding organizations and researchers to answer the most pressing questions for clinicians and public health professionals during the pandemic. It is hoped that these identified priority research themes can help focus the discussion regarding the allocation of limited resources to enhance COVID-19 research in MNCH globally. / Revisión por pares

When our family relocated from South Africa to Abu Dhabi in the United Arab Emirates (UAE) in 2015, I was not prepared for the inception of my personal evolution. Stepping into a classroom as a mathematics teacher, after nearly a decade of being a self-sustainable entrepreneur, forced me to evaluate my motives for becoming a teacher, and shifted my focus to progress and the development of my own pedagogy. Just as I was finding my feet in the British international schooling system in the UAE, the coronavirus disease of 2019 (Covid-19) resulted in a global pandemic and forced all schools to close and roll out distance learning education systems. In turn, this led to a revolution in the teaching profession as I knew it. Both of these contexts paved the way of the title of the thesis: The (r) evolution of a miserable teacher – an autoethnography Autoethnography challenges the canonical manner in which research is conducted, allowing the researcher to use her personal experience (auto) to understand the cultural experience (ethno) better and describe (graphy) the results in narrative form. This might be an oversimplified definition of a complex journey of self-discovery and contextual understanding, but holistically even a definition can evolve. The use of a postmodern perspective throughout the thesis ensured that the data were not limited to a singular paradigm but were rather a culmination of what was relevant at the time of the research. Through reflective and reflexive data collection and construction techniques, a progressive and innovative data graph was developed to visually enhance the (r)evolution of the data description, analysis and evaluation. Conceptual metaphor theory allowed Les Misérables to be the framework and foundation for the data to be constructed. This study contributes to the teachers' collective by combining traditional paradigms in a new, brave, evolutionary way. A personal evolution through resilience and agility became evident through reflective and reflexive data and literature. Evolution is never-ending, but by becoming aware of the effects of personal growth, the process is elevated to a sustainable focus in everyday life. The evolution did not falter when Covid- 19 forced the education sphere to momentarily pause at a possible revolutionary turning point. Distance education allowed politicians, school developers and owners, curriculum writers, principals, school boards and managers, teachers and parents to re-evaluate methodologies, curriculum content and inclusion policies, as well as the everyday implementation of policies and procedures. Literature proves that a revolution was necessary and Covid-19 made this possible by funnelling our expectations into experience. The revolution is far from complete but, similar to evolution, awareness leads to improved practice. / Thesis (PhD)--University of Pretoria, 2021. / Humanities Education / PhD / Unrestricted

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Indiana University-Purdue University Indianapolis (IUPUI) / During 2020, the Indiana State Department of Health conducted a longitudinal study of novel severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus, the cause of COVID-19 disease, to understand the number of past and current infections as well as the prevalence of disease in the State of Indiana by conducting a survey to participants as well as administering testing for exposure to SARS-COV-2. The study consisted of 3 waves of testing, each spread months apart, consisting of a random sample and a non-random sample. The non-random sample was used to ensure the sample population was representative of the state of Indiana and was used as stratum in the logistic regression model, allowing for the adjustment for nonresponse. These finding indicate that persons of non-White race and persons of Hispanic ethnicity had highest risk of exposure to the virus. Understanding the disparity in health in various racial and ethnic populations and addressing how different communities are impacted by the pandemic, as well as working with the community is paramount when attempting to mitigate a pandemic. In addition, understanding the data from the ambient pandemic when instituting measures to mitigate the spread of viruses is also extremely important for managing health emergencies such as the COVID-19 pandemic.

The study aimed to developed a logistic model based on antibody proteins, vaccinations and demographic factors that predicts previous infection in Covid-19. The data set comprised of 2750 individuals from eldercare homes in Sweden, with four test dates executed between October of 2021 and August of 2022.  Exploratory data analysis revealed bimodal patterns in the antibodies against nucleocapsid protein within the non-infected group, raising suspicions of false negatives in the data. Due to the binary nature of the response and to be interpretable for further research, logistic regressions were used to model the relation between predictors and the logit of the response. Because of low performance scores and high probability for the presence of false negatives, K-means clustering algorithm was performed on the data. As a clustering variable, the logarithm of base 2 of the nucleocapsid protein was used, because of its theoretical relationship with previous infection in Covid-19.  Observations were reclassified using the clustering technique, and two new logistic models were fitted to the data. The final model contained polynomial terms to handle the non-linear relationship between the logit of the response and the predictors. We found a significant relationship between the logarithm of 2 of nucleocapsid protein and previous Covid-19 infection in the final model, with high prediction results. We reached an F1-score of 0.94, indicating a well-performing model.  Additionally, an algorithm was created to predict the days since infection, involving the change in nucleocapsid protein from one test date to the next, and a GAM model for fitting a smooth line to the data between nucleocapsid protein as response against the days since infection. Using this algorithm, we reached an absolute mean error between predicted results and actual days since infection of 23 days. This algorithm was later applied to observations reclassified in the clustering process.  In conclusion, the study successfully reclassified false negative observations with previous Covid-19 infection, and fitted a logistic model with high prediction score with F1-score of 0.94. Finally, an algorithm was created that estimated the days since infection with an absolute mean error of 23 days. / Syftet med studien var att utveckla en logistisk modell baserad på antikroppsproteiner, vaccinationer och demografiska faktorer som förutsäger tidigare infektion i Covid-19. Datamängden bestod av 2750 individer från äldreboenden i Sverige, med fyra testdatum utförda mellan oktober 2021 och augusti 2022.  Utforskande dataanalys visade på bimodala mönster i antikroppar mot nukleokapsidprotein inom den icke- infekterade gruppen, vilket gav upphov till misstankar om falskt negativa resultat i datamaterialet. På grund av svarets binära karaktär och för att vara tolkningsbara för vidare forskning användes logistiska regressioner för att modellera förhållandet mellan prediktorer och responsvariabeln. På grund av låga prediktionsresultat och hög sannolikhet av förekomsten av falskt negativa svar utfördes K-means-klusteralgoritmen på datat. Som klustervariabel användes logaritmen av bas 2 för nukleokapsidproteinet, på grund av dess teoretiska samband med tidigare infektion i Covid-19.  Observationerna omklassificerades med hjälp av klustertekniken, och två nya logistiska modeller anpassades till datat. Den slutliga modellen innehöll polynomiala termer för att hantera det icke-linjära förhållandet mellan responsens logit och prediktorerna. Vi fann ett signifikant samband mellan logaritmen av 2 av nuk- leokapsidprotein och tidigare Covid-19-infektion i den slutliga modellen, med ett högt prediktionsresultat. Vi nådde en F1-score på 0.94.  Dessutom skapades en algoritm som predicerade dagar sedan infektion med hjälp av förändringen i nukleokap- sidprotein från ett testdatum till nästa, och en GAM-modell för att anpassa ett glidande medelvärdeslinje till datat mellan nukleokapsidprotein som response mot dagarna sedan infektionen. Med hjälp av denna algoritm nåddes ett absolut medelfel på 23 dagar mellan prediktion och faktiskt tid sedan infektionen. Denna algoritm tillämpades senare på observationer som omklassificerats i klusterprocessen.  Sammanfattningsvis lyckades studien framgångsrikt omklassificera falskt negativa observationer med tidigare Covid-19-infektion och anpassade en logistisk modell med hög prediktionspoäng med en F1-score på 0.94. Slutligen skapades en algoritm som uppskattade dagarna sedan infektionen med ett absolut medelfel på 23 dagar.

Objective- To determine the impact of the COVID-19 pandemic on dental anxiety (DA) and oral health related quality of life (OHRQoL) proxy measures using electronic dental records from Temple University Kornberg School of Dentistry (TUKSoD). Methods- A cross-sectional study comparing data before the COVID-19 Pandemic (G1) (N=4,516) and before (G2) (N=3,995) and after the vaccine was available (G3) (N=3,891) was analyzed for patients 18 and older. The independent variables are age, sex, race, diabetic status, hypertension status, and DA. The dependent variable – OHRQoL – has four components (trouble while chewing, speaking, embarrassment due to dental problems, and pain/ sensitivity) which were converted to a composite score for analysis. OHRQoL, were coded as good, fair, and poor. Mann-Whitney U tests were used to assess outcome differences across the three groups. Statistical significance was set at p<0.05. Results- The proportion of individuals with poor DA in G1, G2, and G3 were about 5%, 3%, and 4% respectively. The proportion of individuals with poor OHRQoL were 8%, 5%, and 4% in G1, G2, and G3, respectively. About 26%, 21%, 19% reported poor social interactions in G1, G2, and G3, respectively. About 1% of individuals in G1, G2, and G3 reported poor speaking. About 8%, 3%, and 5% reported poor chewing in G1, G2, and G3, respectively. About 4% of individuals in G1 and 3% in both G2 and G3 reported poor comfort. Significant differences in DA were observed between G1 vs G2 (p=<0.01) and G1 vs G3 (p=<0.01). Also, significant differences in OHRQoL between G1 vs G2 (p=<0.01), G2 vs G3 (p=0.01), and G1 vs G3 (p-value=<0.01). Associations between DA and OHRQoL were seen across G1, G2, and G3. Hypertension and diabetic status also seemed to be associated with OHRQoL at multiple points in this study. Conclusion- Overall, OHRQoL and dental anxiety improved post COVID-19 vaccine availability compared to the pre-COVID-19 vaccine availability and the pre-COVID-19 pandemic. This could be attributed to increased health consciousness triggered by the COVID-19 events. / Oral Biology

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