While existing research has shown the importance of the three interrelated domains of the wider policy, the school and home/community environments in the development of quality education for learners, this literature does not fully capture the experiences of the refugee population. In this article we focus on a group of Syrian refugees who came as part of the first large cohort that was welcomed in the UK in December 2015. We adapt Tikly’s quality education frameworks and develop a model that highlights not only the importance of the three intersecting environments, but also the specific inputs/processes that are critical to achieving quality education for refugees. In so doing, we stress the critical role of English as a tool for refugee children’s inclusion and integration in schools. Consequently, the contribution of the paper is an understanding of the inputs/processes that are key to the development of quality education for migrant/refugee children.

Wastewater-based epidemiology for SARS-CoV-2 RNA detection in wastewater is desirable for understanding COVID-19 in settings where financial resources and diagnostic facilities for mass individual testing are severely limited. We conducted a rapid review to map research evidence on the utilization of SARS-CoV-2 wastewater surveillance in Africa. We searched PubMed, Google Scholar, and the World Health Organization library databases for relevant reports, reviews, and primary observational studies. Eight studies met the inclusion criteria. Narrative synthesis of the findings from included primary studies revealed the testing methodologies utilized and that detected amount of SARS-CoV-2 viral RNA correlated with the number of new cases in the studied areas. The included reviews revealed the epidemiological significance and environmental risks of SARS-CoV-2 wastewater. Wastewater surveillance data at the community level can be leveraged for the rapid assessment of emerging threats and aid pandemic preparedness. Our rapid review revealed a glaring gap in the primary literature on SARS-CoV-2 wastewater surveillance on the continent, and accelerated and adequate investment into research is urgently needed to address this gap.

The ongoing coronavirus disease 2019 (COVID-19) pandemic remains a significant global public health crisis. The unique evolution of the COVID-19 pandemic has seen social media emerging and growing into an important vehicle for rapid information dissemination. This has in turn given rise to multiple sources of information, leading to what has come to be known as ‘infodemic’, associated with the plethora of misinformation and conspiracy theories. In this perspective, we explore the growth of the social media industry and the impact it has had during the ongoing COVID-19 crisis. We argue that while the multiple information pieces circulating on social media cause misinformation and panic, this might not necessarily and in all the cases influence sustained behaviours in the target population groups. We offer suggestions on how the power of social media can be harnessed and integrated into social and public health for a better digital balance for communication for development.

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HIV and AIDS continue to be major public health concerns globally. Despite significant progress in addressing their impact on the general population and achieving epidemic control, there is a need to improve HIV testing, particularly among men who have sex with men (MSM). This study applied deep and machine learning algorithms such as recurrent neural networks (RNNs), the bagging classifier, gradient boosting classifier, support vector machines, and Naïve Bayes classifier to predict HIV status among MSM using the dataset from the Zimbabwe Ministry of Health and Child Care. RNNs performed better than the bagging classifier, gradient boosting classifier, support vector machines, and Gaussian Naïve Bayes classifier in predicting HIV status. RNNs recorded a high prediction accuracy of 0.98 as compared to the Gaussian Naïve Bayes classifier (0.84), bagging classifier (0.91), support vector machine (0.91), and gradient boosting classifier (0.91). In addition, RNNs achieved a high precision of 0.98 for predicting both HIV-positive and -negative cases, a recall of 1.00 for HIV-negative cases and 0.94 for HIV-positive cases, and an F1-score of 0.99 for HIV-negative cases and 0.96 for positive cases. HIV status prediction models can significantly improve early HIV screening and assist healthcare professionals in effectively providing healthcare services to the MSM community. The results show that integrating HIV status prediction models into clinical software systems can complement indicator condition-guided HIV testing strategies and identify individuals that may require healthcare services, particularly for hard-to-reach vulnerable populations like MSM. Future studies are necessary to optimize machine learning models further to integrate them into primary care. The significance of this manuscript is that it presents results from a study population where very little information is available in Zimbabwe due to the criminalization of MSM activities in the country. For this reason, MSM tends to be a hidden sector of the population, frequently harassed and arrested. In almost all communities in Zimbabwe, MSM issues have remained taboo, and stigma exists in all sectors of society.

Background: The article examines how and why multiple identities are altered, used and discarded by forced migrants.Methods: The research is located in the constructivist paradigm. We used thematic analysis to analyse data gathered through interviews with nineteen forced migrants.Results: We found that, though individual migrants can make deliberate choices about which identities to be associated with, they are constrained in the process by external socio-economic factors that lead them to adopt identities that are perceived to be advantageous to navigate the new social system. Moreover, the construction of forced migrants’ identity includes significant contextuality, transactionality and situatedness.Conclusions: Our research contributes to the literature on migrant identity practice concerning the stigma associated with forced migrant status and the extent to which migrants appraise their reception in exile as undignified. Additionally, examining migrant identities allows the researchers to apprehend the diverse facets of identity as far as migrants are concerned. Future research may draw a larger sample to examine other impactful dimensions of identity fluctuation, e.g. gender, education, social media, the extent of prior trauma, etc.