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- Brewer, T. F., et al.
(author)
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Housing, sanitation and living conditions affecting SARS-CoV-2 prevention interventions in 54 African countries
- 2021
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In: Epidemiology and Infection. - : Cambridge University Press (CUP). - 0950-2688 .- 1469-4409. ; 149
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Journal article (peer-reviewed)abstract
- The feasibility of non-pharmacological public health interventions (NPIs) such as physical distancing or isolation at home to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in low-resource countries is unknown. Household survey data from 54 African countries were used to investigate the feasibility of SARS-CoV-2 NPIs in low-resource settings. Across the 54 countries, approximately 718 million people lived in households with > 6 individuals at home (median percentage of at-risk households 56% (95% confidence interval (CI), 51% to 60%)). Approximately 283 million people lived in households where > 3 people slept in a single room (median percentage of at-risk households 15% (95% CI, 13% to 19%)). An estimated 890 million Africans lack on-site water (71% (95% CI, 62% to 80%)), while 700 million people lacked in-home soap/washing facilities (56% (95% CI, 42% to 73%)). The median percentage of people without a refrigerator in the home was 79% (95% CI, 67% to 88%), while 45% (95% CI, 39% to 52%) shared toilet facilities with other households. Individuals in low-resource settings have substantial obstacles to implementing NPIs for mitigating SARS-CoV-2 transmission. These populations urgently need to be prioritised for coronavirus disease 2019 vaccination to prevent disease and to contain the global pandemic.
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- Nikolov, Nikolay, et al.
(author)
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Conceptualization and scalable execution of big data workflows using domain-specific languages and software containers
- 2021
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In: INTERNET OF THINGS. - : Elsevier BV. - 2543-1536 .- 2542-6605. ; 16, s. 100440-
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Journal article (peer-reviewed)abstract
- Big Data processing, especially with the increasing proliferation of Internet of Things (IoT) technologies and convergence of IoT, edge and cloud computing technologies, involves handling massive and complex data sets on heterogeneous resources and incorporating different tools, frameworks, and processes to help organizations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, requires taking advantage of Cloud infrastructures' elasticity for scalability. In this article, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies, message-oriented middleware (MOM), and a domain-specific language (DSL) to enable highly scalable workflow execution and abstract workflow definition. We demonstrate our system in a use case and a set of experiments that show the practical applicability of the proposed approach for the specification and scalable execution of Big Data workflows. Furthermore, we compare our proposed approach's scalability with that of Argo Workflows - one of the most prominent tools in the area of Big Data workflows - and provide a qualitative evaluation of the proposed DSL and overall approach with respect to the existing literature.
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