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As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1–3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

SummaryAs SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

CloudBank is a cloud access entity founded to enable the computer science research and education communities to harness the profound computational potential of public clouds. By delivering a set of managed services designed to alleviate common points of friction associated with cloud adoption, Cloudbank serves as an integrated service provider to the research and education community. These services include front-line help desk support, cloud solution consulting, training, account management, cost monitoring and optimization support, and automated billing. CloudBank has a multi-cloud pay-per-use billing model and aims to serve the spectrum of cloud users from novice to advanced.

(2002). Complexity, Fragmentation, Uncertainty, and Emergence in Customer Relationship Management. Emergence: Vol. 4, No. 4, pp. 39-50.

Web service technology is moving into the mainstream. HTTP-based integration is proving more useful than prior approaches for integrating heterogeneous and distributed systems. Web service architectures are quickly advancing beyond and becoming more complex than their initial XML (extensible markup language)/SOAP (simple object access protocol)/UDDI (universal description, discovery, and integration) architectures. With added specifications, Web services are creating a service-oriented computing paradigm with their attendant terms and concepts, such as Web service networks, Web service management platforms, and service-oriented architectures (SOA), among others. Aided by Web services, business-to-business (B2B) integration topologies are growing in diversity to support various options for B2B collaboration. Web services are now the primary technical direction enabling this diversification of B2B collaborations (e-collaboration) among value chain partners and customers. They form the foundation for the development of a new generation of B2B applications and the architecture for integrating enterprise applications (Kreger, 2003). Web services promise to increase these partnering companies’ flexibility, agility, competitiveness, as well as opportunities to reduce development cost and time.

The fast-paced, rapidly changing e-business environment, coupled with its emphasis on brand image and the human-computer interface, and the creative nature of Web development teams combine to require changes in traditional system development methodologies. This chapter explores the fit between typical Web-based information system characteristics and existing development methodologies, from the traditional System Development Life Cycle (SDLC) to some of the newer rapid-response models. It concludes that, contrary to common practice in most organizations, one standardized development methodology is not best suited for all, or even most, e-business projects. Fifteen variables that are key to identifying the best methodology for a given e-business project are distilled, and a framework is constructed to aid development teams in the process of formulating a customized development methodology to serve as a basis for project management and control. Projections are made concerning the future of e-business system development methodologies.