The Beta variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in South Africa in late 2020 and rapidly became the dominant variant, causing over 95% of infections in the country during and after the second epidemic wave. Here we show rapid replacement of the Beta variant by the Delta variant, a highly transmissible variant of concern (VOC) that emerged in India and subsequently spread around the world. The Delta variant was imported to South Africa primarily from India, spread rapidly in large monophyletic clusters to all provinces, and became dominant within three months of introduction. This was associated with a resurgence in community transmission, leading to a third wave which was associated with a high number of deaths. We estimated a growth advantage for the Delta variant in South Africa of 0.089 (95% confidence interval [CI] 0.084-0.093) per day which corresponds to a transmission advantage of 46% (95% CI 44-48) compared to the Beta variant. These data provide additional support for the increased transmissibility of the Delta variant relative to other VOC and highlight how dynamic shifts in the distribution of variants contribute to the ongoing public health threat.

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AimThe aim was to design an app-based eLearning tool to provide radiographers with information about the physical basis of MR artefacts and practical elimination or/and minimisation strategies to optimise image quality, and to evaluate the impact of a smartphone app on radiographers’ knowledge.MethodsThe study used the comparison-experimental approach (pre- and post-test). Thirty-five MR radiographers independently reviewed a prepared series of MR images (n = 25). The participants were requested to identify image quality related errors, to specify error-correction strategies and to score how confident they were in their responses. Participants were then divided into experimental (n = 19) and control cohorts (n = 16). The app was provided to the experimental cohort for 3 months; after this period both cohorts re-reviewed the MR image datasets and repeated their identification of image quality errors.ResultsThe results showed a statistically significant difference between control and experimental cohorts relative to participants’ pre- to post-test knowledge level. For the experimental cohort, years of experience, qualification and type of hospital were not associated with radiographer knowledge level and confidence in recognising the presence of an image quality error, naming the error and specifying appropriate correction strategies (p > 0.05).ConclusionThe study identified the potential of the smartphone app as an effective educational tool to support MR radiographers’ knowledge in recognising and characterising MR image quality errors.Key Points• A high level of knowledge to optimise MR image quality is crucial.• Ongoing education in image quality optimisation is required.• The potential role of app as an effective educational tool is identified.