The coronavirus disease 2019 (COVID-19) pandemic has threatened global health and prompted the need for mass vaccination. We aimed to assess the efficacy and effectiveness of COVID-19 vaccines to prevent mortality and reduce the risk of developing severe disease after the 1st and 2nd doses. From conception to 28 June 2021, we searched PubMed, Cochrane, EBSCO, Scopus, ProQuest, Web of Science, WHO-ICTRP, and Google Scholar. We included both observational and randomized controlled trials. The pooled vaccine efficacy and effectiveness following vaccination, as well as their 95 percent confidence intervals (CI), were estimated using the random-effects model. In total, 22 of the 21,567 screened articles were eligible for quantitative analysis. Mortality 7 and 14 days after full vaccination decreased significantly among the vaccinated group compared to the unvaccinated group (OR = 0.10, ([95% CI, 0.04–0.27], I2 = 54%) and (OR = 0.46, [95% CI, 0.35–0.61], I2 = 0%), respectively. The probability of having severe disease one or two weeks after 2nd dose decreased significantly (OR = 0.29 [95% CI, 0.19–0.46], I2 = 25%) and (OR = 0.08 [95% CI, 0.03–0.25], I2 = 74%), respectively. The incidence of infection any time after the 1st and 2nd doses diminished significantly (OR = 0.14 [95% CI, 0.07–0.4], I2 = 100%) and (OR = 0.179 [95% CI, 0.15–0.19], I2 = 98%), respectively. Also, incidence of infection one week after 2nd dose decreased significantly, (OR = 0.04, [95% CI (0.01–0.2], I2 = 100%). After meta-regression, the type of vaccine and country were the main predictors of outcome [non-mRNA type, ß = 2.99, p = 0.0001; country UK, ß = −0.75, p = 0.038; country USA, ß = 0.8, p = 0.02]. This study showed that most vaccines have comparable effectiveness, and it is purported that mass vaccination may help to end this pandemic.

Objective: The presented meta-analysis (MA) aims at identifying the vaccine safety and immunogenicity in published trials about SARS-CoV-2 vaccines. Methods: All relevant publications were systematically searched and collected from different databases (Embase, Scopus, EBSCO, MEDLINE central/PubMed, Science Direct, Cochrane Central Register for Clinical Trials (CENTRAL), Clinical Trials.gov, WHO International Clinical Trials Registry Platform (ICTRP), COVID Trial, COVID Inato, Web of Science, ProQuest Thesis, ProQuest Coronavirus Database, SAGE Thesis, Google Scholar, Research Square, and Medxriv) up to January 10, 2021. The pooled vaccine safety and immunogenicity following vaccination in phase 1 and 2 vaccine clinical trials, as well as their 95% confidence intervals (CI), were estimated using the random-effects model. Results: The predefined inclusion criteria were met in 22 out of 8592 articles. The proportion of anti-severe acute respiratory distress coronavirus 2 (SARS-CoV-2) antibody responses after 7 days among 72 vaccinated persons included in 1 study was 81% (95% CI: 70-89), after 14 days among 888 vaccinated persons included in 6 studies was 80% (95% CI: 58-92), after 28 days among 1589 vaccinated persons included in 6 studies was 63% (95% CI: 59-67), after 42 days among 478 vaccinated persons included in 5 studies was 93% (95% CI: 80-98), and after 56 days among 432 vaccinated persons included in 2 studies was 93% (95% CI: 83-97). Meta regression explains more than 80% of this heterogeneity, where the main predictors were; the inactivated vaccine type (β = 2.027, P = 0.0007), measurement of antibodies at week 1 (β = −4.327, P < 0.0001) and at week 3 of the first dose (β = −2.02, P = 0.0025). Furthermore, the pooled proportion adverse effects 7 days after vaccination was 0.01 (0.08-0.14) for fever, headache 0.23 (0.19-0.27), fatigue 0.10 (0.07-0.13), and 0.18 (0.14-0.23) for muscle pain. Conclusion: Immunogenicity following vaccination ranged from 63% to 93% depending on the time at which the antibody levels were measured.

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Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly.

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