No abstract

The rapid identification and isolation of infected individuals remains a key strategy for controlling the spread of SARS-CoV-2. Frequent testing of populations to detect infection early in asymptomatic or presymptomatic individuals can be a powerful tool for intercepting transmission, especially when the viral prevalence is low. However, RT-PCR testing—the gold standard of SARS-CoV-2 diagnosis—is expensive, making regular testing of every individual unfeasible. Sample pooling is one approach to lowering costs. By combining samples and testing them in groups the number of tests required is reduced, substantially lowering costs. Here we report on the implementation of pooling strategies using 3-d and 4-d hypercubes to test a professional sports team in South Africa. We have shown that infected samples can be reliably detected in groups of 27 and 81, with minimal loss of assay sensitivity for samples with individual Ct values of up to 32. We report on the automation of sample pooling, using a liquid-handling robot and an automated web interface to identify positive samples. We conclude that hypercube pooling allows for the reliable RT-PCR detection of SARS-CoV-2 infection, at significantly lower costs than lateral flow antigen (LFA) tests.

The rapid identification and isolation of infected individuals remains a key strategy for controlling the spread of SARS-CoV-2. Frequent testing of populations to detect infection early in asymptomatic or presymptomatic individuals can be a powerful tool for intercepting transmission, especially when the viral prevalence is low. However, RT-PCR testing – the gold standard of SARS-CoV-2 diagnosis – is expensive, making regular testing of every individual unfeasible. Sample pooling is one approach to lowering costs. By combining samples and testing them in groups the number of tests required is reduced, substantially lowering costs. Here we report on the implementation of pooling strategies using 3-d and 4-d hypercubes to test a professional sports team in South Africa. We have shown that infected samples can be reliably detected in groups of 27 and 81, with minimal loss of assay sensitivity for samples with individual Ct values up to 32. We report on automation of sample pooling, using a liquid-handling robot and an automated web interface to identify positive samples. We conclude that hypercube pooling allows for the reliable RT-PCR detection of SARS-CoV-2 infection, at significantly lower cost than lateral flow antigen (LFA) tests.

No abstract

This paper reports on autonomic computing research, including the development of a self-* proof of concept, for a cloud based environment. It monitors administrative boundaries from within an autonomic manager, with each manager operating in a peer-to-peer mode and utilizing a pulse monitor. The prototype was developed in Java utilizing SNMP to demonstrate the manager's self-situation and environment-awareness of the current state of the whole neighborhood and proves the feasibility of communicating the health of the neighborhood to peer managers using an XML pulse concept. Each manager houses the functionality to enact changes to their neighborhood using SNMP based rules. This enables the capability to provide self-healing, self-configuring, self-optimizing and self-protection to network neighborhoods within cloud computing.