Pooled testing efficiency increases with test frequency

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around x. However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

Optimum Test Suite using Fault-Type Coverage Based Ant Colony Optimization Algorithm

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.

Design improvement of an airbox for a passenger vehicle

The purpose of an airbox is to provide the engine with a clean air flow for combustion. The high velocity of the fluid flow across the airbox will create a pressure drop resulting a decline in the vehicle’s performance. This project collaborates with an Original Equipment Manufacturer (OEM) to develop a numerical simulation model for a new airbox design and to compare its pressure drop with OEM production design. Reducing the pressure drop across the airbox can increase the efficiency of a vehicle, hence, reducing CO2 emissions. This research focuses on the passenger type vehicle as it is the highest source of carbon dioxide (CO2) being emitted for road transportation and these pollutant emissions have also caused many health problems on human. ANSYS Fluent program was used to carry out Computational Fluid Dynamics (CFD) simulation for both OEM and the new design. Then, the same simulation setup was used for the new design. The inlet size of the new design is larger when compared to the OEM design. After analysing both models, it was determined that the main reason behind the pressure loss was caused by the shape of the airbox and turbulent flow inside. The new airbox design shows reduction of 96% in the pressure drop within it and in return, enhancing the performance of the passenger vehicle. This conclude that numerical simulation model is able to provide a good indicator for the designer to choose the best design and proceed with fabrication and conduct actual test, thus saving a lot of prototyping and repeated testing cost.

An Analytical Tool for Constructing and Evaluating Testing Strategic for COVID-19

Background This paper describes the utilization of a mathematical modeling tool for evaluating alternative testing cadences for the SARS-CoV-2 virus that are applicable to any well-contained congregate setting. These settings include long-term care facilities, and public-school systems. Results Variables analyzed include population sizes, contagion factor, and unique testing objectives that congregate settings might have (e.g., differing susceptibilities, or varying underlying health conditions). The tool helps evaluate cost vs benefit for a range of testing cadences (e.g., daily, every 2 days, every 3 days, every week, every 2 weeks every 3 weeks and every 4 weeks) based on use of a commercially available antigen testing kit that costs $5 per test. Conclusions Critical parameters derived as output of the model include total persons tested, average number in quarantine, average percent positives in quarantine, total testing cost, total infections allowed, cases averted, and cost per case averted. These parameters allow public health officials, site managers and/or on-site healthcare workers to optimize testing plans to align with available resources and support fact-based decision making. We also discuss how this tool can work with vaccine roll-out both in the United States and elsewhere.

Pooling in a Pod: A Strategy for COVID-19 Testing to Facilitate a Safe Return to School

The COVID-19 pandemic prompted widespread closures of primary and secondary schools. Routine testing of asymptomatic students and staff members, as part of a comprehensive mitigation program, can help schools open safely. “Pooling in a pod” is a public health surveillance strategy whereby testing cohorts (pods) are based on social relationships and physical proximity. Pooled testing provides a single laboratory test result for the entire pod, rather than a separate result for each person in the pod. During the 2020-2021 school year, an independent preschool–grade 12 school in Washington, DC, used pooling in a pod for weekly on-site point-of-care testing of all staff members and students. Staff members and older students self-collected anterior nares samples, and trained staff members collected samples from younger students. Overall, 12 885 samples were tested in 1737 pools for 863 students and 264 staff members from November 30, 2020, through April 30, 2021. The average pool size was 7.4 people. The average time from sample collection to pool test result was 40 minutes. The direct testing cost per person per week was $24.24, including swabs. During the study period, 4 surveillance test pools received positive test results for COVID-19. A post-launch survey found most parents (90.3%), students (93.4%), and staff members (98.8%) were willing to participate in pooled testing with confirmatory tests for pool members who received a positive test result. The proportion of students in remote learning decreased by 62.2% for students in grades 6-12 ( P < .001) and by 92.4% for students in preschool to grade 5 after program initiation ( P < .001). Pooling in a pod is a feasible, cost-effective surveillance strategy that may facilitate safe, sustainable, in-person schooling during a pandemic.

An Efficient Geometric Search Algorithm of Pandemic Boundary Detection

We consider a scenario where the pandemic infection rate is inversely proportional to the power of the distance between the infected region and the non-infected region. In our study, we analyze the case where the exponent of the distance is 2, which is in accordance with Reilly’s law of retail gravitation. One can test for infection but such tests are costly so one seeks to determine the region of infection while performing few tests. Our goal is to find a boundary region of minimal size that contains all infected areas. We discuss efficient algorithms and provide the asymptotic bound of the testing cost and simulation results for this problem.

O53 Faecal immunochemical testing: cost effective way stratifying symptomatic patients for urgent straight to test investigation

Introduction Quantitative faecal immunochemical test (FIT) offers the opportunity to stratify symptomatic ‘high risk’ colorectal patients for further investigation. Method FIT was introduced in primary care to stratify ‘high risk’ symptomatic patients aged 60 years and above with a change in bowel habit to determine whether an urgent straight to test (STT) CT colonography (CTC) was indicated. All FIT tests were analysed in a national bowel screening hub using the OC-Sensor platform. A result of ≥ 4 μgHb/gFaeces, was used as the cut-off. All FIT results were cross referenced with a prospectively maintained colorectal cancer registry to determine the colorectal cancer detection rate (CRC). Data was analysed from February 2018-December 2019. Result The mean number of total CTC performed per month pre-FIT was 240 (range 185–278) and reduced to 217 (range 183–264) post-implementation (P &lt; 0.05). The number referred under the STT pathway was 167 (range 119–209) reducing to 131 (range 91–153) (P &lt; 0.05), however there was a corresponding rise in the number of non-STT referrals from outpatients 73 (range 44–105) to 85 (range 60–111) (P &lt; 0.05). Conclusion FIT has the potential to reduce the burden on secondary care investigations to exclude bowel cancer. Our experience has shown that a conservative FIT level of &lt; 4ug/ml has reduced numbers of STT referrals by 22%. Take-home Message FIT can be used for symptomatic patients with a change in bowel habits to stratify the need for further investigations. Post-implementation, FIT has reduced STT referral rates and reduced the burden placed on secondary care.