scholarly journals Pooling Strategy Optimization for Accelerating Asymptomatic COVID-19 Screening

2020 ◽  
Author(s):  
Keqin Li
Keyword(s):  
Group Size ◽  
Testing Time ◽  
Significant Progress ◽  
Infected People ◽  
Sample Pooling ◽  
Testing Cost ◽  
Pooling Strategy ◽  
Financial Impacts ◽  
Optimal Group Size ◽  
Great Advance

Abstract Testing has been a major factor that limits our response to the COVID-19 pandemic. The method of sample pooling and group test has recently been introduced. However, it is still not clearly known how to determine the appropriate group size. In this paper, we develop an analytical method and a numerical algorithm to determine the optimal group size, which minimizes the total number of tests, maximizes the speedup of the pooling strategy, and minimizes both time and cost of testing. The optimal group size is determined by the fraction of infected people and independent of the size of the population. Furthermore, both the optimal pooling size and the achieved speedup grow exponentially with the reciprocal of the fraction of infected people, a quite impressive and nontrivial result. Our method is effective in supporting faster and cheaper asymptomatic COVID-19 screening. Our research has important social implications and financial impacts. For example, if the percentage of infected people is 0.001, we can achieve speedup of almost 16, which means that months of testing time can be reduced to days, and over 93% of the testing cost can be saved. Such a result has not been available in the known literature, and is a significant progress and great advance in pooling strategy optimization for accelerating asymptomatic COVID-19 screening.

scholarly journals Optimal group size in microlending

2021 ◽  
Author(s):  
Philip Protter ◽  
Alejandra Quintos
Keyword(s):  
Group Size ◽  
Optimal Group Size

scholarly journals Sample-Pooling Strategy for SARS-CoV-2 Detection among Students and Staff of the University of Sannio

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1166
Author(s):  
Immacolata Polvere ◽  
Elena Silvestri ◽  
Lina Sabatino ◽  
Antonia Giacco ◽  
Stefania Iervolino ◽  
...  
Keyword(s):  
Quantitative Polymerase Chain Reaction ◽  
Virus Detection ◽  
Testing Procedures ◽  
Stem Infection ◽  
In Vitro Diagnostic ◽  
Sample Pooling ◽  
Pooling Strategy ◽  
Asymptomatic Individuals ◽  
The University

Since the beginning of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, it has been clear that testing large groups of the population was the key to stem infection and prevent the effects of the coronavirus disease of 2019, mostly among sensitive patients. On the other hand, time and cost-sustainability of virus detection by molecular analysis such as reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) may be a major issue if testing is extended to large communities, mainly asymptomatic large communities. In this context, sample-pooling and test grouping could offer an effective solution. Here we report the screening on 1195 oral-nasopharyngeal swabs collected from students and staff of the Università degli Studi del Sannio (University of Sannio, Benevento, Campania, Italy) and analyzed by an in-house developed multiplex RT-qPCR for SARS-CoV-2 detection through a simple monodimensional sample pooling strategy. Overall, 400 distinct pools were generated and, within 24 h after swab collection, five positive samples were identified. Out of them, four were confirmed by using a commercially available kit suitable for in vitro diagnostic use (IVD). High accuracy, sensitivity and specificity were also determined by comparing our results with a reference IVD assay for all deconvoluted samples. Overall, we conducted 463 analyses instead of 1195, reducing testing resources by more than 60% without lengthening diagnosis time and without significant losses in sensitivity, suggesting that our strategy was successful in recognizing positive cases in a community of asymptomatic individuals with minor requirements of reagents and time when compared to normal testing procedures.

scholarly journals Critical Aspects Concerning the Development of a Pooling Approach for SARS-CoV-2 Diagnosis Using Large-Scale PCR Testing

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 902
Author(s):  
Daniel Cruceriu ◽  
Oana Baldasici ◽  
Loredana Balacescu ◽  
Stefana Gligor-Popa ◽  
Mirela Flonta ◽  
...  
Keyword(s):  
Large Scale ◽  
Rna Extraction ◽  
Diagnostic Assay ◽  
Assay Sensitivity ◽  
Infected People ◽  
Multiple Parameters ◽  
Accurate Procedure ◽  
Manual Methods ◽  
Pooling Strategy ◽  
Critical Aspects

The primary approach to controlling the spread of the pandemic SARS-CoV-2 is to diagnose and isolate the infected people quickly. Our paper aimed to investigate the efficiency and the reliability of a hierarchical pooling approach for large-scale PCR testing for SARS-CoV-2 diagnosis. To identify the best conditions for the pooling approach for SARS-CoV-2 diagnosis by RT-qPCR, we investigated four manual methods for both RNA extraction and PCR assessment targeting one or more of the RdRp, N, S, and ORF1a genes, by using two PCR devices and an automated flux for SARS-CoV-2 detection. We determined the most efficient and accurate diagnostic assay, taking into account multiple parameters. The optimal pool size calculation included the prevalence of SARS-CoV-2, the assay sensitivity of 95%, an assay specificity of 100%, and a range of pool sizes of 5 to 15 samples. Our investigation revealed that the most efficient and accurate procedure for detecting the SARS-CoV-2 has a detection limit of 2.5 copies/PCR reaction. This pooling approach proved to be efficient and accurate in detecting SARS-CoV-2 for all samples with individual quantification cycle (Cq) values lower than 35, accounting for more than 94% of all positive specimens. Our data could serve as a comprehensive practical guide for SARS-CoV-2 diagnostic centers planning to address such a pooling strategy.

scholarly journals Changes in group size during resource shifts reveal drivers of sociality across the tree of life

2020 ◽  
Author(s):  
Albert B. Kao ◽  
Amanda K. Hund ◽  
Fernando P. Santos ◽  
Jean-Gabriel Young ◽  
Deepak Bhat ◽  
...  
Keyword(s):  
Group Size ◽  
Social Benefit ◽  
Tree Of Life ◽  
Strong Indication ◽  
Evolutionary Mechanisms ◽  
Resource Abundance ◽  
Dominant Mechanism ◽  
Individual Fitness ◽  
Model Predictions ◽  
Optimal Group Size

ABSTRACTFrom biofilms to whale pods, organisms have repeatedly converged on sociality as a strategy to improve individual fitness. Yet, it remains challenging to identify the most important drivers—and by extension, the evolutionary mechanisms—of sociality for particular species. Here, we present a conceptual framework, literature review, and model demonstrating that the direction and magnitude of the response of group size to sudden resource shifts provides a strong indication of the underlying drivers of sociality. We catalog six functionally distinct mechanisms related to the acquisition of resources, and we model these mechanisms’ effects on the survival of individuals foraging in groups. We find that whether, and to what degree, optimal group size increases, decreases, or remains constant when resource abundance declines depends strongly on the dominant mechanism. Existing empirical data support our model predictions, and we demonstrate how our framework can be used to predict the dominant social benefit for particular species. Together, our framework and results show that a single easily measurable characteristic, namely, group size under different resource abundances, can illuminate the potential drivers of sociality across the tree of life.

scholarly journals Evaluation of a Sample Pooling Strategy for Sars-cov-2 Using Real Time PCR

2021 ◽  
Author(s):  
Annet M Nankya ◽  
Luke Nyakarahuka ◽  
Stephen Balinandi ◽  
John Kayiwa ◽  
Julius Lutwama ◽  
...  
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Virus Disease ◽  
Laboratory Diagnosis ◽  
Turnaround Time ◽  
Limited Resources ◽  
Asymptomatic Patients ◽  
Sample Pooling ◽  
Sample Testing ◽  
Pooling Strategy

Abstract Back ground: Corona Virus Disease 2019 (COVID 19) in Uganda was first reported in a male traveler from Dubai on 21st March, 2020 shortly after WHO had announced the condition as a global pandemic. Timely laboratory diagnosis of COVID -19 for all samples from both symptomatic and asymptomatic patients was observed as key in containing the pandemic and breaking the chain of transmission. However, there was a challenge of limited resources required for testing SARS-COV-2 in low and middle income countries. To mitigate this, a study was conducted to evaluate a sample pooling strategy for COVI-19 using real time PCR. The cost implication and the turn around time of pooled sample testing versus individual sample testing were also compared.Methods: In this study, 1260 randomly selected samples submitted to Uganda Virus Research Institute for analysis were batched in pools of 5, 10, and 15. The pools were then extracted using a Qiagen kit. Both individual and pooled RNA were screened for the SARS-COV-2 E gene using a Berlin kit. Results: Out of 1260 samples tested, 21 pools were positive in pools of 5 samples, 16 were positive in pools of 10 and 14 were positive in pools of 15 samples. The study also revealed that the pooling strategy helps to save a lot on resources, time and expands diagnostic capabilities without affecting the sensitivity of the test in areas with low SARS-COV-2 prevalence.Conclusion: This study demonstrated that the pooling strategy for COVID-19 reduced on the turnaround time and there was a substantial increase in the overall testing capacity with limited resources as compared to individual testing.

Optimal Group Size

2018 ◽  
pp. 1-2
Author(s):  
Jeannine Holmes ◽  
Suzanne MacDonald
Keyword(s):  
Group Size ◽  
Optimal Group Size

Optimal group size and northern bobwhite coveys

2003 ◽  
Vol 66 (2) ◽  
pp. 377-387 ◽  
Author(s):  
Christopher K Williams ◽  
R.Scott Lutz ◽  
Roger D Applegate
Keyword(s):  
Group Size ◽  
Northern Bobwhite ◽  
Optimal Group Size

Relatedness and conflict over optimal group size

1994 ◽  
Vol 9 (4) ◽  
pp. 117-119 ◽  
Author(s):  
Bruce H. Rannala ◽  
Charles R. Brown
Keyword(s):  
Group Size ◽  
Optimal Group Size

scholarly journals Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Viruses ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 769 ◽  
Author(s):  
Ahmed Sedik ◽  
Abdullah M Iliyasu ◽  
Basma Abd El-Rahiem ◽  
Mohammed E. Abdel Samea ◽  
Asmaa Abdel-Raheem ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Testing Time ◽  
Learning Models ◽  
Accurate Identification ◽  
Infected People ◽  
Corona Virus

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

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