Positive COVID-19 Research Articles

Development and evaluation of a Lateral flow immunoassay (LFIA) prototype for the detection of IgG anti-SARS-CoV-2 antibodies

Lateral flow immunoassays (LFIA) for antibody detection represent cost-effective and user-friendly tools for serology assessment. This study evaluated a new LFIA prototype developed with a recombinant chimeric antigen from the spike/S and nucleocapsid/N proteins to detect anti-SARS-CoV-2 IgG antibodies. The evaluation of LFIA sensitivity and specificity used 811 serum samples from 349 hospitalized, SARS-CoV-2 RT-qPCR positive COVID-19 patients, collected at different time points and 193 serum samples from healthy controls. The agreement between ELISA results with the S/N chimeric antigen and LFIA results was calculated. The LFIA prototype for SARS-CoV-2 using the chimeric S/N protein demonstrated 85 % sensitivity on the first week post symptoms onset, reaching 94 % in samples collected at the fourth week of disease. The agreement between LFIA and ELISA with the same antigen was 92.7 %, 0.827 kappa Cohen value (95 % CI [0.765–0.889]). Further improvements are needed to standardize the prototype for whole blood use. The inclusion of the novel chimeric S + N antigen in the COVID-19 IgG antibody LFIA demonstrated optimal agreement with results from a comparable ELISA, highlighting the prototype's potential for accurate large-scale serologic assessments in the field in a rapid and user-friendly format.

The science behind the nose: correlating volatile organic compound characterisation with canine biodetection of COVID-19.

The SARS-CoV-2 pandemic stimulated the advancement and research in the field of canine scent detection of COVID-19 and volatile organic compound (VOC) breath sampling. It remains unclear which VOCs are associated with positive canine alerts. This study aimed to confirm that the training aids used for COVID-19 canine scent detection were indeed releasing discriminant COVID-19 VOCs detectable and identifiable by gas chromatography (GC-MS). Inexperienced dogs (two Labradors and one English Springer Spaniel) were trained over 19 weeks to discriminate between COVID-19 infected and uninfected individuals and then independently validated. Getxent tubes, impregnated with the odours from clinical gargle samples, used during the canines' maintenance training process were also analysed using GC-MS. Three dogs were successfully trained to detect COVID-19. A principal components analysis model was created and confirmed the ability to discriminate between VOCs from positive and negative COVID-19 Getxent tubes with a sensitivity of 78% and a specificity of 77%. Two VOCs were found to be very predictive of positive COVID-19 cases. When comparing the dogs with GC-MS, F1 and Matthew's correlation coefficient, correlation scores of 0.69 and 0.37 were observed, respectively, demonstrating good concordance between the two methods. This study provides analytical confirmation that canine training aids can be safely and reliably produced with good discrimination between positive samples and negative controls. It is also a further step towards better understanding of canine odour discrimination of COVID-19 as the scent of interest and defining what VOC elements the canines interpret as "essential".

Evaluation of whole blood profile as a tool in COVID-19 diagnosis and screening. A cross-sectional study

Backgroung: the COVID-19 epidemic began in December 2019, and the shortage of diagnostic resources has affected the reported data on the number of cases, resulting in variations in reported cases between countries. This situation underscores the necessity for a deeper understanding of SARS-CoV-2 pathophysiology, including blood profiles and potential predictors. Methods: hematological variables were studied in 200 patients diagnosed with COVID-19, before the vaccination period started. We analyzed hemogram parameters: erythrocytes, hemoglobin, mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV) mean corpuscular hemoglobin concentration (MCHC), hematocrit, red cell distribution width (RDW), platelets, mean platelet volume (MPV), leukocytes, neutrophils, lymphocytes, neutrophils/lymphocytes ratio and Platelet-to-Lymphocyte Ratio (PLR), up to 9 days after positive result for COVID-19. Results: the positive COVID-19 group presented a higher mean age, as well as a higher frequency of male individuals. Erythrocyte, hemoglobin, hematocrit and MCH values were significantly lower, while RDW and PLR showed higher values in the positive group. Leukocytes, neutrophil and the neutrophil/lymphocyte ratio presented higher values in the positive COVID-19 group. Conclusion: data showed that the hemogram, a low-cost, minimally invasive exam, supports diagnosis and screening of COVID-19, allowing better evaluation of the disease course and assisting medical decisions facing lack of resources in a pandemic situation.

Effectiveness of the Sanofi/GSK (VidPrevtyn Beta) and Pfizer-BioNTech (Comirnaty Original/Omicron BA.4-5) bivalent vaccines against hospitalisation in England

The Sanofi/GSK AS03-adjuvanted (VidPrevtyn Beta) vaccine and the Pfizer-BioNTech mRNA (Comirnaty Original/Omicron BA.4-5) bivalent vaccine were offered to adults aged 75 years and over in England from 3rd April 2023. This is the first time an adjuvanted COVID-19 vaccine has been administered as part of a UK COVID-19 vaccination programme. In clinical trials, antibody levels generated were comparable with mRNA vaccines but there are no real-world data on the effectiveness or duration of protection. We used a test-negative case-control study design to estimate the incremental vaccine effectiveness of the Sanofi/GSK and Pfizer bivalent BA.4-5 boosters against hospitalisation amongst those aged 75 years and older in England. Cases (those testing positive) and controls (those testing negative) were identified from the national COVID-19 PCR testing data undertaken in hospital settings. The study period included tests from 3rd April 2023 to 27th August 2023. Tests were linked to the COVID-19 vaccination register and to the national hospital admission database, restricting to those with an acute respiratory infection coded in the primary diagnosis field. Vaccine effectiveness was estimated using multivariable logistic regression amongst those who had last received an autumn 2022 booster given at least 3 months prior. The test result was the outcome and vaccination status the exposure. Analyses were adjusted for week of test, gender, age, clinical risk group status, care home resident status, region, index of multiple deprivation, ethnicity, influenza vaccination status and recent COVID-19 positivity. There were 14,169 eligible tests from hospitalised individuals aged 75 years and older; 3005 cases (positive tests) and 11,164 controls (negative tests). Effectiveness was highest in the period 9-13 days post vaccination for both manufacturers at about 50%; 43.7% (95% CI, 20.1-60.3%) and 56.1% (95% CI, 25.2-74.2%) for Sanofi/GSK and Pfizer BA.4-5, respectively. There was evidence of waning with a reduction to about 30% for both manufacturers after 5-9 weeks. The longest time interval post vaccination for which we were able to estimate effectiveness was 10+ weeks post vaccination, at which point vaccine effectiveness was 17.6% (95% CI,-3.6 to 34.5%) and 37.9% (95% CI, 13.2-55.5%) for the Sanofi/GSK and Pfizer BA.4-5 boosters, respectively. Both boosters provided good protection against hospitalisation amongst older adults. The finding that the adjuvanted vaccine targeting the distant Beta strain had similar effectiveness to the bivalent mRNA vaccine targeting more closely matched Omicron sub-lineages is notable and highlights the need for further real-world studies into the effectiveness of vaccines from different vaccine platforms and formulations in the presence of matched and unmatched strains. No external funding.

Accelerated tumor progression after COVID-19 infection in patients with glioblastoma: A retrospective case-control study.

We observed rapid tumor progression following COVID-19 infection among patients with glioblastoma and sought to systematically characterize their disease course in a retrospective case-control study. Using an institutional database, we retrospectively identified a series of COVID-19-positive glioblastoma cases and matched them by age and sex 1:2 to glioblastoma controls who had a negative COVID-19 test during their disease course. Demographic and clinical data were analyzed. Hyperprogression was defined using modified response evaluation criteria in solid tumors criteria. Time to progression and overall survival were estimated using the Kaplan-Meier method. Thirty-two glioblastoma cases with positive COVID-19 testing were matched to 64 glioblastoma controls with negative testing; age, sex, and molecular profiles did not differ between groups. Progression events occurred in 27 cases (84%) and 46 controls (72%). Of these, 14 cases (52%) presented with multifocal disease or leptomeningeal disease at progression compared with 10 controls (22%; P = .0082). Hyperprogression was identified in 13 cases (48%) but only 4 controls (9%; P = .0001). Cases had disease progression at a median of 35 days following COVID-19 testing, compared with 164 days for controls (P = .0001). Median survival from COVID-19 testing until death was 8.3 months for cases but 17 months for controls (P = .0016). Median overall survival from glioblastoma diagnosis was 20.7 months for cases and 24.6 months for controls (P = .672). Patients with glioblastoma may have accelerated disease progression in the first 2 months after COVID-19 infection. Infected patients should be monitored vigilantly. Future investigations should explore tumor-immune microenvironment changes linking tumor progression and COVID-19.

Assessment of Cobalt Binding Activity of Albumin in COVID-19 Patients and its Correlation with CRP

Background: Covid 19 pandemic has thrown a big challenge in terms of available manpower, space and financial recourses at all levels of healthcare facilities. The disease manifestation ranges from asymptomatic to severe but a pro-inflammatory milieu is the underlying ongoing event. Detection of pro- inflammatory markers in early stages of the disease is instrumental to avert future complications.Method: For the present study 35 RT-PCR positive Covid patients were chosen from indoor admissions along with 35 age and sex matched controls. C Reactive Protein (CRP) and cobalt binding activity of albumin measured in both groups and compared statistically.The values of CRP is elevated in cases (153.92± 87.56) as compared to controls (3.71± 3.07) which is statistically significant (p value= 0.001).Elevation of Cobalt binding activity of albumin is statistically significant (p value=0.0001) in cases (151.04± 37.38) when compared to controls (81.96± 29.49). Moreover elevation of cobalt binding activity of albumin bears moderate positive correlation (r=0.609) with the elevation of CRP levels.Conclusion: As laboratory assay of cobalt binding albumin has manifold advantages, when compared to assay of CRP, the former may emerge as a promising marker if similar result are obtained with a larger study population.

Predictive Modeling of Infection from Chest X-ray Images with ResNet34 and Deep learning CNN approach

The timely and precise detection of infected persons is still a major issue in the ongoing fight against the COVID-19 epidemic. With its ability to provide information on the severity and development of the illness, chest X-ray imaging has become a useful diagnostic and surveillance technique for COVID-19 pneumonia. This paper suggests a unique method for predictive modeling of COVID-19 infection from chest X-ray pictures by using the capabilities of deep learning, namely ResetNet architecture. In image classification tasks, ResNet34, a convolutional neural network (CNN) variation, has shown impressive performance, particularly in situations with little training data. Here, we report a comprehensive training and validation protocol for the ResetNet model using a large dataset of chest X-ray images that includes both positive and negative COVID-19 instances. To improve feature extraction, the dataset is preprocessed. All data size: 5856,Train size: 4684, Val size: 585, Test size: 587. To reduce overfitting and boost generalization, data augmentation methods are used. Our suggested method uses chest X-ray pictures to reliably distinguish COVID-19 patients from non-COVID-19 cases by using deep learning's discriminative capabilities. The result of model with TRAINING: Epoch: 19, Loss: 0.08099747663349896, Accuracy: 97.11and TEST: Epoch: 19, Loss: 0.08553645759820938, Accuracy: 96.76.

Has the COVID-19 pandemic shock transmitted to the u.s. stock market: Evidence using bootstrap (A)symmetric fourier granger causality test in quantiles

This study investigates whether the COVID-19 shocks transmitted to the U.S. stock market were symmetric or asymmetric using bootstrap quantile asymmetric Granger causality tests with or without the Fourier functions. The results indicate that while low quantiles of stock returns do not respond to COVID-19 shocks, the latter is a powerful predictor of high quantiles of stock returns. Besides, only positive COVID-19 shocks are potent predictors of high quantiles of stock market return. In contrast, adverse shocks adequately predict low quantiles of stock return. These findings have important policy implications for investors and policymakers who implement strategies for market stabilization.

COVID-19 and Its Impact on Back Pain in the Eastern Province of Saudi Arabia.

Background Low back pain (LBP) is a common musculoskeletal condition that affects individuals worldwide, causing difficulties in daily tasks and social interactions. It can be categorized based on chronicity, with acute, subacute, and chronic forms. The causes of backache vary among patients and can include inflammatory conditions, radiculopathy, pregnancy, trauma, osteoporosis, nerve root compression, cancer, plexopathy, infection, and other spinal diseases. Aim The aim is to investigate the association between COVID-19 infection and LBP between all Saudi adults and foreign adults who had positive COVID-19 tests in the eastern region of Saudi Arabia. Methods A cross-sectional study was conducted in the Eastern Province of Saudi Arabia over the period from March 2023 to August 2023.Participants were selected by using a convenience sampling method, asample (n=500) of individuals. The structured questionnaire was used to gather information on sociodemographic variablesand COVID-related features. All the statistical calculations were performed using the SPSS software (by IBM) version 29.0.0. Results 482 participants completed the questionnaire. Out of 482 participants, the majority were females with a number of 372 (77.2%) aged between 20 and 29 years (38.4%). Out of the remaining participants, 110 (22.8%) were males. Most of the participants with a number of 301 (62.4%) were from the Hasa province. This was followed by Qatif (79, 16.4%), Dammam (56, 11.6%), Jubail (25, 5.2%), and others (21, 4.4%). The study revealed that 10.1% of participants reported experiencing back pain. The duration of backaches varied among respondents, with 122 (25.3%) experiencing them from a day to a week, 28 (5.8%) enduring them for six weeks, and 65 (13.5%) reporting a duration of six to 12 weeks. The majority, comprising 267 (55.4%) respondents, were uncertain about the period of their backaches. The prevalence of COVID-19 infection among the participants was 357 (74.1%), and 477 (99.0%) had been vaccinated against COVID-19. Approximately 44.4% of the participants experienced back pain, and out of those, 28.2% reported having pain during quarantine. Among the individuals with back pain, 24.7% attributed it to COVID-19. Conclusion This study highlights the significant correlation between back pain and COVID-19, even after the resolution of other symptoms. It underscores the importance of further research into the long-term effects and mechanisms of this association. The findings emphasize the need for healthcare professionals to consider back pain as a potential aspect of the post-COVID-19 symptom profile, ensuring comprehensive care for affected individuals.

Mental Health Concerns During COVID-19: An Observational Study Among a Predominantly Black Community in New York City.

This study examined the prevalence of mental health concerns and its association with COVID-19, selected social determinants of health, and psychosocial risk factors in a predominantly racial/ethnic minoritized neighborhood in New York City. Adult Harlem residents (N = 393) completed an online cross-sectional survey from April to September 2021. The Patient Health Questionnaire (PHQ-4) and the Post-Traumatic Stress Disorder (PC-PTSD) were used to evaluate mental health concerns. Poisson regression with robust variance quantified the associations of interests via prevalence ratios (PR) and 95% confidence intervals (CI). Two-thirds (66.4%) of theresidents reported experiencing mental health concerns, including PTSD (25.7%), depression (41.2%), and anxiety (48.1%). Residents with low-income housing status (PR = 1.16; 95% CI 1.01, 1.34), alcohol misuse (PR = 1.68; 95% CI 1.40, 2.01), food insecurity (PR = 1.23; 95% CI 1.07, 1.42), exposure to interpersonal violence (PR = 1.33; 95% CI 1.08, 2.65), and experience of discrimination (PR = 1.53, 95% CI 1.23-1.92) were more likely to report mental health concerns. Better community perception of the police (PR = 0.97, 95% CI 0.95, 0.99) was associated with fewer mental health concerns. No associations were observed for employment insecurity, housing insecurity, or household COVID-19 positivity with mental health concerns. This study showed a high prevalence of mental health concerns in a low-income racial/ethnic minoritized community, where COVID-19 and social risk factors compounded these concerns. Harlem residents face mental health risks including increased financial precarity, interpersonal violence, and discrimination exposure. Interventions are needed to address these concurrent mental health and psychosocial risk factors, particularly in racial/ethnic minoritized residents.

Relation between pre-existing quality management measures and prevention and containment of COVID-19 outbreaks in 159 nursing homes in Tuscany: a mixed methods study

BackgroundNursing homes were often the focus of COVID-19 outbreaks. Many factors are known to influence the ability of a nursing home to prevent and contain a COVID-19 outbreak. The role...

COVID-19 Infection and Medicines in Pregnancy in Canada

IntroductionAlthough over 100 million pregnant women worldwide are at risk of infection with SARS-CoV-2, little data exists on the impact of COVID-19 and related treatments on maternal/neonatal health.Objectives1) To quantify the prevalence of medication use in pregnancy to treat COVID-19; 2) To quantify and compare the risk of adverse pregnancy/neonatal outcomes in those with and without COVID-19.MethodsIn the Canadian Mother-Child population-based cohort (CAMCCO), two key sub-cohorts were identified using prospective data collection of medical services, prescription drugs, hospitalization archives data, and COVID-19 surveillance testing program (02/28/2020-2021). The first cohort included all pregnant women with at least one completed trimester of pregnancy during the study period regardless of pregnancy status (delivery, induced/planned or spontaneous abortion); this cohort was further stratified on COVID-19 status. The second cohort included all non-pregnant women (aged 15-45) with a positive COVID-19 test. COVID-19 infection in pregnant or non-pregnant women was assessed using COVID-19 test results or ICD-10CM codeU07.1 from hospital data. COVID-19 severity was categorized based on hospital admission. Women were considered exposed to COVID-19 medications if they filled at least one prescription for a medicine included in the WHO list in the 30 days pre- or 30 days post-COVID-19 positive test/diagnosis. Considering potential confounders, association between COVID-19 during pregnancy, treated vs not, and perinatal outcomes were quantified using log-binomial regression models.Results150,345 pregnant women (3,464 (2.3%) had COVID-19), and 112,073 non-pregnant women with COVID-19 diagnoses were included. Pregnant women with COVID-19 were more likely to have severe infections compared to non-pregnant women with COVID-19 (11.4% vs 1.6%, p< 0.001). The most frequent medications used in pregnancy to treat COVID-19 were antibacterials (13.96%), psychoanaleptics (7.35%), and medicines for obstructive airway disease (3.20%). In pregnancy COVID-19 was associated with spontaneous abortions (adjRR 1.76, 95%CI 1.3, 2.25), gestational diabetes (adjRR 1.52, 95%CI 1.18, 1.97), prematurity (adjRR 1.30, 95%CI 1.01, 1.67), NICU admissions (adjRR 1.32, 95%CI 1.10, 1.59); COVID-19 severity was increasing these risks but COVID-19 treatment with study medications reduced all risks.ConclusionsSeverity of COVID-19 was greater in pregnancy. Antibacterials, psychoanaleptics, and medicines for obstructive airway disease were the most used overall. Severe COVID-19 in pregnancy was associated with higher risks of adverse maternal, and neonatal outcomes.Disclosure of InterestNone Declared

Estimating the incidence of COVID-19, influenza and respiratory syncytial virus infection in three regions of Queensland, Australia, winter 2022: findings from a novel longitudinal testing-based sentinel surveillance programme

ObjectiveThe 2022 Australian winter was the first time that COVID-19, influenza and respiratory syncytial virus (RSV) were circulating in the population together, after two winters of physical distancing, quarantine and...

COVID-19 Diagnosis from Blood Gas Using Multivariate Linear Regression

With the impact of the COVID-19 outbreak, almost all scientists and nations began to show great interest in the subject for a long time. Studies in the field of outbreak, diagnosis and prevention are still ongoing. Issues such as methods developed to understand the spread mechanisms of the disease, prevention measures, vaccine and drug research are among the top priorities of the world agenda. The accuracy of the tests applied in the outbreak management has become extremely critical. In this study, it is aimed to obtain a function that finds the positive or negative COVID-19 test from the blood gas values of individuals by using Machine Learning methods to contribute to the outbreak management. Using the Multivariate Linear Regression (MLR) model, a linear function is obtained to represent the COVID-19 dataset taken from the Van province of Turkey. The data set obtained from Van Yüzüncü Yıl University Dursun Odabaş Medical Center consists of blood gas analysis samples (109 positive, 1146 negative) taken from individuals. It is thought that the linear function to be obtained by using these data will be an important method in determining the test results of individuals. Gradient Descent optimization methods are used to find the optimum values of the coefficients in the function to be obtained. In the study, the RMSProp optimization algorithm has a success rate of 58-91.23% in all measurement methods, and it is seen that it is much more successful than other optimization algorithms.

Individualized Prediction of SARS-CoV-2 Infection in Mexico City Municipality during the First Six Waves of the Pandemic.

After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63-82%), sensitivity of 52% (18-71%), and specificity of 84% (71-92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations.

Acil Abdominal Cerrahi Yapılan COVID-19 Hastalarında Postoperatif Komplikasyonların Öngördürücüleri

Amaç: COVID-19 dünya çapında hızla yayılması ve pandemi haline gelmesi, cerrahi hastalarda postoperatif komplikasyonların insidansında da artışa neden oldu. Acil ameliyata alınan hastalarda COVID-19 pozitifliğinin ameliyat sonrası komplikasyonları etkileyebileceği açıktır. Bu çalışmada acil abdominal cerrahi uygulanan COVID-19 hastalarında postoperatif komplikasyonlar için öngördürücü faktörlerin değerlendirilmesi amaçlanmıştır.
 Gereç ve Yöntemler: Bu çalışmada acil abdominal cerrahi uygulanan ve perioperatif 72 saat içerisinde COVID-19 Polimeraz Zincir Reaksiyonu (PCR) testi pozitif sonuçlanan 52 hasta retrospektif olarak değerlendirildi.
 Bulgular: Çalışmaya dahil edilen hastaların medyan yaşı 50.5’di (aralık: 18-83). 30 günlük mortalite oranı %15.4 olarak saptandı. Postoperatif komplikasyon oranı %23.1’di. Postoperatif komplikasyon durumu ile yaş (p=0.003) ve preoperatif hemoglobin değeri (p=0.001) arasında anlamlı bir ilişki saptandı. Yaş ve hemoglobinin birlikte değerlendirilmesinin postoperatif komplikasyonlar için sensivitesinin %91.7, spesifitesinin %87.5 olduğu görüldü (p

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Encouraging Positive Dialog Toward COVID-19 Vaccines on Social Media Using Hope Appeals, Celebrity Types, and Emoticons

ABSTRACT This study examined the effects of hope appeals, celebrity endorser types, and valence of emoticons on COVID-19 vaccine confidence, hesitancy, and intention to receive the COVID-19 vaccine. The potential moderating effects of parasocial identification were also assessed. Experiment 1 was a 2 (hope appeals: high vs. low personal relevance) x 3 (celebrity types: politicians vs. scientists vs. athletes) between-subjects experiment with a sample of U.S. adults (N = 363). Experiment 2 was a 2 (hope appeals: high vs. low personal relevance) x 3 (valence of emoticons: positive vs. neutral vs. negative) between-subjects experiment with a sample of U.S. adults (N = 369). Findings indicated that COVID-19 vaccine messages with politician endorsement and low personal relevance significantly influenced intention to receive the COVID-19 vaccine. Moreover, COVID-19 vaccine messages with neutral emoticons significantly impacted COVID-19 vaccine confidence and COVID-19 vaccine hesitancy. Parasocial identification with the endorser was also found to play a role in affecting vaccine hesitancy. Implications for future research in vaccine communication are discussed.

Medical education during the COVID-19 pandemic: a reflection on the JHUSOM experience

BackgroundWe sought to understand the relative risk of COVID-19 infection and identify risk factors for infection to identify targets for mitigation among medical students.MethodsAn observational cohort study of Johns Hopkins School of Medicine students was conducted from June 2020 to July 2021. Blood samples were collected and tested at three visits to assess for antibodies against SARS-CoV-2. Additionally, a questionnaire was administered at each visit to collect demographic information and assess potential social and behavioral risk factors.Results264 students enrolled in the study, and 38 participants completed all study requirements by study end. Roughly 6% of the first- and second-year classes had a reported positive COVID-19 test compared to 5% of third- and fourth-year students. By visit 3, 92% of medical students had detectable antibodies against COVID-19 compared to 4% during the study enrollment period. From study enrollment to visit 3, there was a 10-fold increase in the percentage of students reporting attending large social gatherings and dining in restaurants.ConclusionsOverall, few COVID-19 cases were found among medical students, even those on clinical rotations. As the study progressed, students reported engaging in higher-risk social behaviors in conjunction with increasing vaccination rates among students.

A deep learning-based approach for predicting COVID-19 diagnosis

This paper focuses on forecasting the total count of confirmed COVID-19 cases in Saudi Arabia through a range of methodologies, including ARIMA, mathematical modeling, and deep learning network (DQN) techniques. Its primary aim is to anticipate the verified COVID-19 cases in Saudi Arabia, aiding in decision-making for life-saving interventions by enhancing awareness of COVID-19 infection. Mathematical modeling and ARIMA are employed for their efficacy in forecasting, while DQN approaches, particularly through comparative analysis, are utilized for prediction. This comparative analysis evaluates the predictive capacities of ARIMA, mathematical modeling, and DQN techniques, aiming to pinpoint the most reliable method for forecasting positive COVID-19 cases. The modeling encompasses COVID-19 cases in Saudi Arabia, the United Kingdom (UK), and Tunisia (TU) spanning from 2020 to 2021. Predicting the number of individuals likely to test positive for COVID-19 poses a challenge, requiring adherence to fundamental assumptions in mathematical and ARIMA projections. The proposed methodology was implemented on a local server. The DQN algorithm formulates a reward function to uphold target functional performance while balancing training and testing periods. The findings indicate that DQN technology surpasses conventional approaches in efficiency and accuracy for predictions.