scholarly journals Group Testing Performance Evaluation for SARS-COV-2 Massive Scale Screening and Testing

2020 ◽  
Author(s):  
Ozkan Ufuk Nalbantoglu
Keyword(s):  
Scale Up ◽  
Group Testing ◽  
Error Correcting Codes ◽  
Molecular Testing ◽  
Decoding Algorithms ◽  
Testing Performance ◽  
Massive Scale ◽  
Low Prevalence ◽  
Current Paradigm ◽  
Column Group

ABSTRACTThe capacity of current molecular testing convention does not allow high-throughput and community level scans of COVID-19 infections. The diameter in current paradigm of shallow tracing is unlikely to reach the silent clusters that might be as important as the symptomatic cases in the spread of the disease. Group testing is a feasible and promising approach when the resources are scarce and when a relatively low prevalence regime is observed on the population. We employed group testing with a sparse random pooling scheme and conventional group test decoding algorithms both for exact and inexact recovery. Our simulations showed that significant reduction in per case test numbers (or expansion in total test numbers preserving the number of actual tests conducted) for very sparse prevalence regimes is available. Currently proposed COVID-19 group testing schemes offer a gain up to 10X scale-up. There is a good probability that the required scale up to achieve massive scale testing might be greater in certain scenarios. We investigated if further improvement is available, especially in sparse prevalence occurrence where outbreaks are needed to be avoided by population scans. Our simulations show that sparse random pooling can provide improved efficiency gains compared to row-column group testing or Reed-Solomon error correcting codes. Therefore, we propose that special designs for different scenarios could be available and it is possible to scale up testing capabilities significantly.

scholarly journals Group Testing for Severe Acute Respiratory Syndrome– Coronavirus 2 to Enable Rapid Scale-up of Testing and Real-Time Surveillance of Incidence

2020 ◽  
Vol 222 (6) ◽  
pp. 903-909 ◽  
Author(s):  
Christopher D Pilcher ◽  
Daniel Westreich ◽  
Michael G Hudgens
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Positive Predictive Value ◽  
High Throughput ◽  
Scale Up ◽  
Group Testing ◽  
Fixed Number ◽  
Molecular Testing ◽  
Viral Dynamics ◽  
True Positive ◽  
Pooling Strategies

Abstract High-throughput molecular testing for severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) may be enabled by group testing in which pools of specimens are screened, and individual specimens tested only after a pool tests positive. Several laboratories have recently published examples of pooling strategies applied to SARS-CoV-2 specimens, but overall guidance on efficient pooling strategies is lacking. Therefore we developed a model of the efficiency and accuracy of specimen pooling algorithms based on available data on SAR-CoV-2 viral dynamics. For a fixed number of tests, we estimate that programs using group testing could screen 2–20 times as many specimens compared with individual testing, increase the total number of true positive infections identified, and improve the positive predictive value of results. We compare outcomes that may be expected in different testing situations and provide general recommendations for group testing implementation. A free, publicly-available Web calculator is provided to help inform laboratory decisions on SARS-CoV-2 pooling algorithms.

scholarly journals A Population-Specific Optimized GeneXpert Pooling Algorithm for Chlamydia trachomatis and Neisseria gonorrhoeae To Reduce Cost of Molecular Sexually Transmitted Infection Screening in Resource-Limited Settings

2020 ◽  
Vol 58 (9) ◽  
Author(s):  
Sarah Connolly ◽  
William Kilembe ◽  
Mubiana Inambao ◽  
Ana-Maria Visoiu ◽  
Tyronza Sharkey ◽  
...  
Keyword(s):  
Trichomonas Vaginalis ◽  
Molecular Testing ◽  
Resource Limited Settings ◽  
Syphilis Infection ◽  
Syndromic Management ◽  
Sexually Transmitted ◽  
Factors Associated ◽  
Resource Limited ◽  
The Cost ◽  
Low Prevalence

ABSTRACT The sexually transmitted infections (STIs) chlamydia (CT) and gonorrhea (NG) are often asymptomatic in women and undetected by syndromic management, leading to complications such as pelvic inflammatory disease, infertility, and ectopic pregnancy. Molecular testing, such as the GeneXpert CT/NG assay, is highly sensitive, but cost restraints preclude implementation of these technologies in resource-limited settings. Pooled testing is one strategy to reduce the cost per sample, but the extent of savings depends on disease prevalence. The current study describes a pooling strategy based on identification of sociodemographic and laboratory factors associated with CT/NG prevalence in a high-risk cohort of Zambian female sex workers and single mothers conducted from 2016 to 2019. Factors associated with testing positive for CT/NG via logistic regression modeling included city, younger age, lower education, long-acting reversible contraception usage, Trichomonas vaginalis infection, bacterial vaginosis, and incident syphilis infection. Based on these factors, the study population was stratified into high-, intermediate-, and low-prevalence subgroups and tested accordingly—individually, pools of 3, or pools of 4, respectively. The cost per sample was reduced from $18 to as low as $9.43 in the low-prevalence subgroup. The checklist tool and pooling approach described can be used in a variety of treatment algorithms to lower the cost per sample and increase access to molecular STI screening. This is particularly valuable in resource-limited settings to detect and treat asymptomatic CT/NG infections missed by traditional syndromic management.

scholarly journals Hamming Codes: Error Reducing Techniques

2021 ◽  
Vol 9 (11) ◽  
pp. 1972-1974
Author(s):  
Rohitkumar R Upadhyay
Keyword(s):  
Lower Bound ◽  
Error Correction ◽  
Coding Theory ◽  
Hamming Distance ◽  
Error Reduction ◽  
Error Correcting Codes ◽  
Significant Other ◽  
Hamming Codes ◽  
Decoding Algorithms ◽  
Reducing Properties

Abstract: Hamming codes for all intents and purposes are the first nontrivial family of error-correcting codes that can actually correct one error in a block of binary symbols, which literally is fairly significant. In this paper we definitely extend the notion of error correction to error-reduction and particularly present particularly several decoding methods with the particularly goal of improving the error-reducing capabilities of Hamming codes, which is quite significant. First, the error-reducing properties of Hamming codes with pretty standard decoding definitely are demonstrated and explored. We show a sort of lower bound on the definitely average number of errors present in a decoded message when two errors for the most part are introduced by the channel for for all intents and purposes general Hamming codes, which actually is quite significant. Other decoding algorithms are investigated experimentally, and it generally is definitely found that these algorithms for the most part improve the error reduction capabilities of Hamming codes beyond the aforementioned lower bound of for all intents and purposes standard decoding. Keywords: coding theory, hamming codes, hamming distance

scholarly journals Projection Decoding of Some Binary Optimal Linear Codes of Lengths 36 and 40

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
Lucky Galvez ◽  
Jon-Lark Kim
Keyword(s):  
Linear Codes ◽  
Cyclic Codes ◽  
Minimum Weight ◽  
Error Correcting Codes ◽  
Decoding Algorithm ◽  
Decoding Algorithms ◽  
Syndrome Decoding ◽  
Optimal Linear ◽  
Optimal Linear Codes ◽  
Efficient Decoding

Practically good error-correcting codes should have good parameters and efficient decoding algorithms. Some algebraically defined good codes, such as cyclic codes, Reed–Solomon codes, and Reed–Muller codes, have nice decoding algorithms. However, many optimal linear codes do not have an efficient decoding algorithm except for the general syndrome decoding which requires a lot of memory. Therefore, a natural question to ask is which optimal linear codes have an efficient decoding. We show that two binary optimal [ 36 , 19 , 8 ] linear codes and two binary optimal [ 40 , 22 , 8 ] codes have an efficient decoding algorithm. There was no known efficient decoding algorithm for the binary optimal [ 36 , 19 , 8 ] and [ 40 , 22 , 8 ] codes. We project them onto the much shorter length linear [ 9 , 5 , 4 ] and [ 10 , 6 , 4 ] codes over G F ( 4 ) , respectively. This decoding algorithm, called projection decoding, can correct errors of weight up to 3. These [ 36 , 19 , 8 ] and [ 40 , 22 , 8 ] codes respectively have more codewords than any optimal self-dual [ 36 , 18 , 8 ] and [ 40 , 20 , 8 ] codes for given length and minimum weight, implying that these codes are more practical.

scholarly journals Antimicrobial Resistance of Neisseria gonorrhoeae Isolates from High-Risk Men in Johannesburg, South Africa

2020 ◽  
Vol 64 (11) ◽  
Author(s):  
Liteboho D. Maduna ◽  
Marleen M. Kock ◽  
Brian M. J. W. van der Veer ◽  
Oscar Radebe ◽  
James McIntyre ◽  
...  
Keyword(s):  
South Africa ◽  
High Risk ◽  
Antimicrobial Resistance ◽  
Neisseria Gonorrhoeae ◽  
Genetic Relatedness ◽  
Scale Up ◽  
Sub Saharan Africa ◽  
Molecular Testing ◽  
Content Type ◽  
Urethral Discharge

ABSTRACT Neisseria gonorrhoeae antimicrobial drug resistance has emerged worldwide; however, the situation in sub-Saharan Africa is not well documented. We investigated the molecular epidemiology and occurrence of antimicrobial resistance in Neisseria gonorrhoeae infections in two core transmission groups of men in Johannesburg, South Africa. We recruited men who have sex with men (MSM) presenting with urethral discharge and men with recurrent episodes of urethral discharge. Molecular testing and culture for N. gonorrhoeae were performed, followed by antimicrobial susceptibility testing. Whole-genome sequencing (WGS) was used to identify resistance-conferring mutations and to determine the genetic relatedness of the isolates. In all, 51 men were recruited; 42 (82%) had N. gonorrhoeae infections. Most gonococcal isolates were resistant to ciprofloxacin (78%) and tetracycline (74%); 33% were penicillin resistant. All gonococcal isolates were susceptible to cephalosporins and spectinomycin. Azithromycin resistance was observed in 4 (15%) isolates (epidemiological cutoff), all with mutations in the mtrR promoter region. Most of the isolates (19/27) harbored the gonococcal genetic island, which is associated with antimicrobial resistance. WGS revealed a diverse epidemic with mostly novel NG-STAR (70%) and NG-MAST (70%) sequence types. Thus, we demonstrate a high prevalence of antimicrobial resistance in Neisseria gonorrhoeae strains obtained from high-risk men in South Africa. The introduction of diagnostics and scale-up of surveillance are warranted to prevent the emergence of multidrug-resistant infections.

Two Alleles with RHCE*nt818C>T Change Encode the Low Prevalence Rh Antigen STEM.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 3159-3159 ◽  
Author(s):  
Christine Halter-Hipsky ◽  
Kim Hue-Roye ◽  
Gail Coghlan ◽  
Christine Lomas-Francis ◽  
Marion E. Reid
Keyword(s):  
South African ◽  
Molecular Basis ◽  
Index Case ◽  
Conflicts Of Interest ◽  
Direct Sequencing ◽  
Molecular Testing ◽  
Variable Expression ◽  
New Findings ◽  
Low Prevalence ◽  
Original Index

Abstract Abstract 3159 Poster Board III-96 Background According to the original (and only) report, the low prevalence Rh antigen, STEM, is associated with an altered e phenotype. Approximately 65% of hrS– and 30% of hrB– RBCs from South African donors are STEM+. STEM has a variable expression, which is an inherited characteristic. Anti-STEM has induced mild HDFN (Marais, et al., Transf Med 1993;3:35-41). The purpose of this study was to determine the molecular basis associated with STEM expression. Material and Methods Blood samples and reagents were from our collections. Hemagglutination and DNA extraction were performed by standard methods. Molecular testing included direct sequencing and cloning of cDNA, AS-PCR, PCR-FRLP, and sequencing specific exons of gDNA. Results Three STEM+ samples (including the original index case) had RHCE*ceBI [ce 48C (16C), 712G (238V), 818T (273V), 1132G (378V)] (Noizat-Pirenne, et al., Blood 2002;100:4223-31) and 6 had a new allele, which we name RHCE*ceSM (ce 48C, 712G, 818T). In contrast, 8 STEM– samples (which included hrS– and hrB– samples) did not have the RHCE*818C>T change. RBCs with the ceBI phenotype expressed STEM more strongly than those with the ceSM phenotype. Conclusions The previously reported allele RHCE*ceBI and a new allele, RHCE*ceSM, encode the STEM antigen. This study also revealed other new findings: (i) ceSM encodes a weaker expression of STEM than does ceBI, which explains the previously reported variable expression, (ii) provides an explanation for why not all hrS– and hrB– RBCs express STEM. RBCs with ceAR, ceMO, and ceEK, phenotypes are hrS– STEM–, and RBCs with ceS phenotypes type hrB–, STEM–, (iii) ceBI and ceSM do not express hrS but do express hrB. It is likely that anti-hrS made by hrS– STEM– people (ceAR, ceMO, ceEK) will be incompatible with hrS– STEM+ RBCs, and vice versa. Our findings provide a means to positively identify the STEM+ phenotypes, which, by hemagglutination, is virtually impossible due to the dearth of anti-STEM. Further, it provides a tool to provide suitable antigen-negative RBC products to a patient who has made an ‘e-like’ antibody. Disclosures No relevant conflicts of interest to declare.

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