Changing Trends in COVID-19 Symptomatology: A Survey-Based Analysis.

India currently ranks the highest in the world with over 3.86 lakhs new COVID-19 cases per day. With a spike in the number of cases in the second wave of COVID-19 in 2021 compared to the first wave of the outbreak in 2020, there have been varied clinical manifestations among masses. This study aimed to determine the changing trends in prevalence of COVID-19 symptoms during the pandemic. A cross-sectional study among 166 individuals was carried out using a self-designed survey-based questionnaire. Two groups were made on the basis of symptoms and compared: Group A- patients who tested COVID-19 positive in 2020 and Group B- patients who tested COVID-19 positive in 2021. 130 participants (78.31%) had tested positive for COVID-19, out of which 110 (84.62%) were symptomatic and 20 (15.38%) were asymptomatic. Fever was the most common presenting symptom (27.69%) followed by difficulty in breathing (24.62%). Group A individuals (n = 37), reported fever as the most common presenting symptom (45.95%), followed by body ache (13.51%); while those in Group B (n = 93) reported difficulty in breathing (33.33%) followed by fever (20.43%). The most common general symptoms were fever and difficulty in breathing while sore throat, cough and anosmia were the most common ENT symptoms. 57.83% had been vaccinated out of which 38.55% experienced symptoms post-vaccination. The prevalence of symptoms in the first and second wave of the pandemic can help in better understanding of the changing symptomatology of SARS-CoV-2 virus.

Post Covid Vaccination Symptoms among Individuals Vaccinated in Different Vaccination Centres of a Tertiary Care Hospital in Mangalore

Background: SARS-CoV-2 infection can be effectively reduced with vaccination. Despite the numerous reported post vaccination symptoms, there is very limited literature. The study was intended to evaluate the proportion of vaccine recipients experiencing post vaccination symptoms. The aim also included assessing the incidence of immediate post vaccination symptoms to the first dose/second dose of COVID-19 vaccine and to study the spectrum of post-vaccination symptom profile (within 30 minutes and 24 hours). Material(s) and Method(s): The cross-sectional study was carried out on 833 individuals, above 18 years who received covid vaccine from the different vaccination centres under KMC HOSPITAL, Mangalore. Data collected through online google survey form was analysed using IBM SPSS 25 software. Result(s): Among the 833 vaccine recipients only 236 individuals received 2 doses of vaccine.70% received COVISHIELD while 30% received COVAXIN. Vaccinated individuals experienced more symptoms within 24 hours (72.4% after first dose,45.8% after second dose), compared to those who experienced symptoms within 30 minutes (31% after first dose,19.1% after second dose). Proportion of post vaccination symptoms were higher with first dose COVISHILED (within 24 hours-77.7%) and significantly lower with second dose of COVISHIELD (within 30 minutes-13%). Most common symptoms were muscle pain, headache, tiredness, fever and least common symptoms were vomiting, diarrhoea, cough. Conclusion(s): Vaccinated individuals experienced more symptoms within 24 hours after first dose. Majority of the symptoms were minor, which subsided with oral paracetamol or without any intervention. This study will help medical professionals to educate the public, dispel incorrect information and reduce vaccine hesitancy. Copyright © 2022 University of Agriculture. All rights reserved.

Predicting death risk analysis in fully vaccinated people using novel extreme regression-voting classifier.

Vaccination for the COVID-19 pandemic has raised serious concerns among the public and various rumours are spread regarding the resulting illness, adverse reactions, and death. Such rumours can damage the campaign against the COVID-19 and should be dealt with accordingly. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people by utilizing the available data. This study focuses on the prognosis of three significant events including 'not survived', 'recovered', and 'not recovered' based on the adverse events followed by the second dose of the COVID-19 vaccine. Extensive experiments are performed to analyse the efficacy of the proposed Extreme Regression- Voting Classifier model in comparison with machine learning models with Term Frequency-Inverse Document Frequency, Bag of Words, and Global Vectors, and deep learning models like Convolutional Neural Network, Long Short Term Memory, and Bidirectional Long Short Term Memory. Experiments are carried out on the original, as well as, a balanced dataset using Synthetic Minority Oversampling Approach. Results reveal that the proposed voting classifier in combination with TF-IDF outperforms with a 0.85 accuracy score on the SMOTE-balanced dataset. In line with this, the validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy.

EVALUATION OF COVID-19 VACCINES EFFICACY IN IRAQI PEOPLES.

OBJECTIVE: The aim: The present study was carried out on patients recovered from COVID-19, including those patients who have taken vaccine and those who have not. PATIENTS AND METHODS: Materials and methods: The patients were recruited via an online panel and surveyed at different regions of Iraq from June 1, 2021, to August 30, 2021. RESULTS: Results: Our results demonstrated that the highest percentage of people recommended Pfizer vaccine followed by Sinopharm, while AstraZeneca vaccine was least recommended. CONCLUSION: Conclusions: The efficacy of different vaccines differed significantly; the highest effectiveness was observed with Pfizer vaccine followed by AstraZeneca and Sinopharm with effectiveness ranging from 94%, 89%, and 74%, respectively. Further, the highest percentage of re-infected patients was observed with Sinopharm vaccine followed by Astra Zeneca and Pfizer vaccine, respectively. Also, the highest percent of re-infection with masking used was seen in the case of Sinopharm vaccine followed by AstraZeneca and Pfizer vaccine. Although, we observed that post-vaccination symptoms were lowest than pre-vaccination symptoms, the percent of asymptomatic cases post-vaccination was highest than pre-vaccination cases for all vaccines.

Safety and Effectiveness of COVID-19 Vaccines: Results from a Cross-Sectional Survey among Staff, Workers and Students at an Egyptian University.

Mass vaccination is the most effective strategy against the spread of the COVID-19 pandemic. However, concerns about the vaccine's safety and effectiveness remain a huge obstacle to vaccine acceptance. The aim of the present study was to explore different COVID-19 vaccine outcomes, including the development of adverse events and/or COVID-19 infection following COVID-19 vaccination. A cross-sectional study was conducted by distributing an online survey targeting staff and students at the British university in Egypt. A total of 637 participants fully completed the survey. Of these, 609 (95.6%) participants received the COVID-19 vaccine. Only 12.6% of the total vaccinated participants reported COVID-19 infection after vaccination. Of these, only 2.8% reported having severe symptoms while 9.9% reported having no or mild symptoms. The most common side effects reported after the first vs. second dose were headache (36.3% vs. 14.6%), tiredness and fatigue (26.9% vs. 10.7), and fever (25.6% vs. 6.7%). In conclusion, the present study explored different COVID-19 vaccine outcomes where the overall incidence of side effects is higher after the first dose than after the second dose. There is a relationship between COVID-19 vaccines' side effects and gastrointestinal disorders, gender, and the type of COVID-19 vaccine. Post-vaccination symptoms were more frequently reported in women compared to men and more frequent with viral vector vaccines compared to other types. The effectiveness of different types of COVID-19 vaccines was confirmed by the lower incidence rate of post-vaccination COVID-19 infection.

The COVID-19 vaccination experience in Bangladesh: Findings from a cross-sectional study.

OBJECTIVES: Vaccination rollout against COVID-19 has started in developed countries in early December 2020. Mass immunization for poor or low-income countries is quite challenging before 2023. Being a lower-middle-income country, Bangladesh has begun a nationwide COVID-19 vaccination drive in early February 2021. Here, we aimed to assess the opinions, experiences, and adverse events of the COVID-19 vaccination in Bangladesh. METHODS: We conducted this online cross-sectional study from 10 February 2021, to 10 March 2021, in Bangladesh. A self-reported semi-structured survey questionnaire was used using Google forms. We recorded demographics, disease history, medication records, opinions and experiences of vaccination, and associated adverse events symptoms. RESULTS: We observed leading comorbid diseases were hypertension (25.9%), diabetes (21.1%), heart diseases (9.3%), and asthma (8.7%). The most frequently reported adverse events were injection site pain (34.3%), fever (32.6%), headache (20.2%), fatigue (16.6%), and cold feeling (15.4%). The chances of having adverse events were significantly higher in males than females (p = 0.039). However, 36.4% of respondents reported no adverse events. Adverse events usually appeared after 12 h and went way within 48 h of vaccination. Besides, 85.5% were happy with the overall vaccination management, while 88.0% of the respondents recommended the COVID-19 vaccine for others for early immunization. CONCLUSION: According to the present findings, reported adverse events after the doses of Covishield in Bangladesh were non-serious and temporary. In Bangladesh, the early vaccination against COVID-19 was possible due to its prudent vaccine deal, previous mass vaccination experience, and vaccine diplomacy.

COVID-19 Vaccination in Patients with Pulmonary Arterial Hypertension and Chronic Thromboembolic Pulmonary Hypertension: Safety Profile and Reasons for Opting against Vaccination.

The incidence of COVID-19 infection in patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) is similar to that in the general population, but the mortality rate is much higher. COVID-19 vaccination is strongly recommended for PAH/CTEPH patients. The aim of our cross-sectional study was to identify reasons why PAH/CTEPH patients refused vaccination against COVID-19. Moreover, we assessed the safety profile of approved COVID-19 vaccines in PAH/CTEPH patients. We examined 261 patients (164 PAH patients and 97CTEPH patients) with a median age of 60 (18-92) years, 62% of which were female. Sixty-one patients (23%) refused to be vaccinated. The main reason for unwillingness to be vaccinated was anxiety about adverse events (AEs, 61%). Age and fear of COVID-19 in the univariate analysis and age ≥60 years in the multivariate regression analysis were factors that impacted willingness to be vaccinated (OR = 2.5; p = 0.005). AEs were reported in 61% of vaccinated patients after the first dose and in 40.5% after the second dose (p = 0.01). The most common reported AEs were pain at the injection site (54.5%), fever (22%), fatigue (21%), myalgia (10.5%), and headache (10%). A lower percentage of AEs was reported in older patients (OR = 0.3; p = 0.001). The COVID-19 vaccines are safe for PAH/CTEPH patients. The results obtained in this study may encourage patients of these rare but severe cardio-pulmonary diseases to get vaccinated against COVID-19.

Side Effects and Perceptions Following COVID-19 Vaccination in Jordan: A Randomized, Cross-Sectional Study Implementing Machine Learning for Predicting Severity of Side Effects.

BACKGROUND: Since the coronavirus disease 2019 (COVID-19) was declared a pandemic, there was no doubt that vaccination is the ideal protocol to tackle it. Within a year, a few COVID-19 vaccines have been developed and authorized. This unparalleled initiative in developing vaccines created many uncertainties looming around the efficacy and safety of these vaccines. This study aimed to assess the side effects and perceptions following COVID-19 vaccination in Jordan. METHODS: A cross-sectional study was conducted by distributing an online survey targeted toward Jordan inhabitants who received any COVID-19 vaccines. Data were statistically analyzed and certain machine learning (ML) tools, including multilayer perceptron (MLP), eXtreme gradient boosting (XGBoost), random forest (RF), and K-star were used to predict the severity of side effects. RESULTS: A total of 2213 participants were involved in the study after receiving Sinopharm, AstraZeneca, Pfizer-BioNTech, and other vaccines (38.2%, 31%, 27.3%, and 3.5%, respectively). Generally, most of the post-vaccination side effects were common and non-life-threatening (e.g., fatigue, chills, dizziness, fever, headache, joint pain, and myalgia). Only 10% of participants suffered from severe side effects; while 39% and 21% of participants had moderate and mild side effects, respectively. Despite the substantial variations between these vaccines in the presence and severity of side effects, the statistical analysis indicated that these vaccines might provide the same protection against COVID-19 infection. Finally, around 52.9% of participants suffered before vaccination from vaccine hesitancy and anxiety; while after vaccination, 95.5% of participants have advised others to get vaccinated, 80% felt more reassured, and 67% believed that COVID-19 vaccines are safe in the long term. Furthermore, based on the type of vaccine, demographic data, and side effects, the RF, XGBoost, and MLP gave both high accuracies (0.80, 0.79, and 0.70, respectively) and Cohen's kappa values (0.71, 0.70, and 0.56, respectively). CONCLUSIONS: The present study confirmed that the authorized COVID-19 vaccines are safe and getting vaccinated makes people more reassured. Most of the post-vaccination side effects are mild to moderate, which are signs that body's immune system is building protection. ML can also be used to predict the severity of side effects based on the input data; predicted severe cases may require more medical attention or even hospitalization.