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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic spread quickly. Vaccines are now being distributed to stop the infectious spread and halt fatalities. The Pfizer-BioNTech vaccine was the first mRNA-based vaccine introduced to boost immunity against COVID-19; however, it could lead to various adverse reactions. Therefore, the aim of this study was to assess the prevalence of Pfizer vaccine side effects among participants. MethodsThis was a multicenter cross-sectional study that was performed using a non-probability sampling method. The study period was about six months from March 1, 2022, to August 31, 2022. A total of 1000 participants who received two doses of the Pfizer vaccine met the inclusion criteria. Demographic details of participants, for example, gender, age, comorbidities, Pfizer vaccine with both doses along with booster dose, previous exposure to coronavirus disease 2019 (COVID-19) infection, and the incidence of any local and systemic side effects following the first and second doses of vaccine, were reported. ResultsThe study findings showed that out of 1000 participants, 644 (64.4%) were males and 356 (35.6%) were females; their mean age was 43.06±14.98 years. Among them, 280 (28.0%) had hypertension and 356 (35.6%) had diabetes. Following the first dose of the Pfizer vaccine, burning at the injection site and fever were the most commonly reported side effects in 704 (70.4%) and 700 (70.0%) participants, respectively. Following the second dose of the Pfizer vaccine, muscle pain was the most commonly reported side effect in 628 (62.8%) participants. ConclusionThis study concluded that the most frequent adverse effects of the Pfizer vaccine were burning at the injection site, fever, pain at the injection site, muscle pain, swelling at the injection site, and joint pain. Moreover, the first dose was associated with more side effects than the second dose.

was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two-and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-ofthe-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis systems achieved excellent results with the VGG-19 architecture. The two-classifier diagnosis system achieved high sensitivity for COVID-19, with 99.5% and 100% on DATASET-1 and DATASET-2, respectively. The three-classifier diagnosis system achieves high sensitivity for COVID-19, with 98.4% and 100% on DATASET-1 and DATASET-2, respectively. The high sensitivity of these diagnostic systems for COVID-19 will significantly improve the speed and precision of COVID-19 diagnosis.

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