scholarly journals COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ola Brynildsrud
Keyword(s):  
Random Sampling ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Optimal Sample ◽  
Sample Pooling

scholarly journals Covid-19 prevalence estimation by random sampling in the wider population - Optimal sample pooling under varying assumptions about true prevalence

2020 ◽  
Author(s):  
Ola Brynildsrud
Keyword(s):  
Confidence Intervals ◽  
Random Sampling ◽  
Large Fraction ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Optimal Sample ◽  
Wide Range ◽  
Prevalence Estimates ◽  
Test Criteria ◽  
Sample Pooling

ABSTRACTThe number of confirmed Covid-19 cases in a population is used as a coarse measurement for the burden of disease. However, this number depends heavily on the sampling intensity and the various test criteria used in different jurisdictions. A wide range of sources indicate that a large fraction of cases go undetected. Estimates of the true prevalence of Covid-19 can be made by random sampling in the wider population. Here we use simulations to explore confidence intervals of prevalence estimates under different sampling intensities and degrees of sample pooling.

scholarly journals COVID-19 prevalence estimation by random sampling in the population - Optimal sample pooling under varying assumptions about true prevalence

2020 ◽  
Author(s):  
Ola Brynildsrud
Keyword(s):  
Random Sampling ◽  
Large Fraction ◽  
Positive Sample ◽  
Individual Level ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Optimal Sample ◽  
Sample Pooling ◽  
Many Sources ◽  
The Cost

Abstract Background The number of confirmed COVID-19 cases divided by population size is used as a coarse measurement for the burden of disease in a population. However, this fraction depends heavily on the sampling intensity and the various test criteria used in different jurisdictions, and many sources indicate that a large fraction of cases tend to go undetected. Methods Estimates of the true prevalence of COVID-19 in a population can be made by random sampling. Here I use simulations to explore confidence intervals of prevalence estimates under different sampling strategies, exploring optimal sample sizes and degrees of sample pooling at a range of true prevalence levels. Results Sample pooling can greatly reduce the total number of tests required for prevalence estimation. In low-prevalence populations, it is theoretically possible to pool hundreds of samples with only marginal loss of precision. Even when the true prevalence is as high as 10% it can be appropriate to pool up to 15 samples, although this comes with the cost of not knowing which patients were positive. Sample pooling can be particularly beneficial when the test has imperfect specificity can provide more accurate estimates of the prevalence than an equal number of individual-level tests. Conclusion Sample pooling should be considered in COVID-19 prevalence estimation efforts.

scholarly journals COVID-19 prevalence estimation by random sampling in population - Optimal sample pooling under varying assumptions about true prevalence

2020 ◽  
Author(s):  
Ola Brynildsrud
Keyword(s):  
Random Sampling ◽  
Large Fraction ◽  
Rt Pcr ◽  
Individual Level ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Prevalence Estimates ◽  
Sample Pooling ◽  
Many Sources ◽  
Low Prevalence

Abstract Background: The number of confirmed COVID-19 cases divided by population size is used as a coarse measurement for the burden of disease in a population. However, this fraction depends heavily on the sampling intensity and the various test criteria used in different jurisdictions, and many sources indicate that a large fraction of cases tend to go undetected. Methods: Estimates of the true prevalence of COVID-19 in a population can be made by random sampling and pooling of RT-PCR tests. Here I use simulations to explore how experiment sample size and degrees of sample pooling impact precision of prevalence estimates and potential for minimizing the total number of tests required to get individual-level diagnostic results.Results: Sample pooling can greatly reduce the total number of tests required for prevalence estimation. In low-prevalence populations, it is theoretically possible to pool hundreds of samples with only marginal loss of precision. Even when the true prevalence is as high as 10% it can be appropriate to pool up to 15 samples. Sample pooling can be particularly beneficial when the test has imperfect specificity by providing more accurate estimates of the prevalence than an equal number of individual-level tests.Conclusion: Sample pooling should be considered in COVID-19 prevalence estimation efforts.

Diagnosis, management and prevalence estimation of gonorrhoea: influences of Aptima Combo 2 assay with alternative target confirmation

2009 ◽  
Vol 20 (5) ◽  
pp. 315-319 ◽  
Author(s):  
M Mahto ◽  
S Zia ◽  
D Ritchie ◽  
H Mallinson
Keyword(s):  
Chlamydia Trachomatis ◽  
Laboratory Data ◽  
Standard Of Care ◽  
Substantial Contribution ◽  
Sexually Transmitted ◽  
True Prevalence ◽  
Medicine Clinic ◽  
Prevalence Estimation ◽  
M 10 ◽  
Primary Test

Case-notes and laboratory data were retrospectively reviewed for influences of dual testing by Aptima Combo 2 (AC2) for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) on the diagnosis, management and prevalence estimation of gonorrhoea in the genitourinary (GU) medicine clinic and community. NG positives by AC2 were confirmed by Aptima Gonococcus assay. Unconfirmed positives were rare. Our study showed that in the GU medicine clinic, AC2 detected about 20% extra cases of NG beyond culture. For best standard of care, NG culture and microscopy are still required in some patients to ensure that treatment is rapid and appropriate. Compared to self-referral at the GU medicine clinic, community tests made a substantial contribution to the overall number of NG cases found (40 community versus 35 Macclesfield GU medicine clinic). The ratio of female to male NG cases found was significantly higher ( P = 0.002) in the community (13 M, 27 F) than at the GU medicine clinic (25 M, 10 F). In the community, over 60% of NG infections occurred in chlamydia-negative patients. The overall prevalence of NG in the GU medicine clinic was 1.3%, the true prevalence being much lower at 0.9% on primary test. Prevalence in the community was 0.4%. Data from dual testing in the community can clarify NG prevalence beyond the existing KC60 (sexually transmitted infections) reports.

scholarly journals Optimal sample pooling: an efficient tool against SARS-CoV-2

2020 ◽  
Author(s):  
Rakesh Sharma ◽  
Saurabh Goyal ◽  
Priti Bist
Keyword(s):  
Limited Resources ◽  
Efficient Tool ◽  
Multiple Strategies ◽  
Optimal Sample ◽  
Health Agencies ◽  
The World ◽  
Practical Challenge ◽  
Sample Pooling ◽  
Successful Approach ◽  
User Friendly

The SARS-CoV-2 pandemic situation has presented multiple imminent challenges to the nations around the globe. While health agencies around the world are exploring various options to contain the spread of this fatal viral infection, multiple strategies and guidelines are being issued to boost the fight against the disease. Identifying and isolating infected individuals at an early phase of the disease has been a very successful approach to stop the chain of transmission. But this approach faces a practical challenge of limited resources. Sample pooling solves this enigma by significantly improving the testing capacity and result turn around time while using no extra resources. However, the general sample pooling method also has the scope of significant improvements. This article describes a process to further optimize the resources with optimal sample pooling. This is a user-friendly technique, scalable on a national or international scale. A mathematical model has been built and validated for its performance using clinical data.

Comparison of prevalence estimation ofMycobacterium aviumsubsp.paratuberculosisinfection by sampling slaughtered cattle with macroscopic lesionsvs.systematic sampling

2012 ◽  
Vol 141 (7) ◽  
pp. 1536-1544 ◽  
Author(s):  
J. ELZE ◽  
E. LIEBLER-TENORIO ◽  
M. ZILLER ◽  
H. KÖHLER
Keyword(s):  
Lymph Nodes ◽  
Mycobacterium Avium ◽  
Random Sampling ◽  
Detection Rate ◽  
Clinical Signs ◽  
Systematic Sampling ◽  
Systematic Random Sampling ◽  
Prevalence Estimation

SUMMARYThe objective of this study was to identify the most reliable approach for prevalence estimation ofMycobacterium aviumssp.paratuberculosis(MAP) infection in clinically healthy slaughtered cattle. Sampling of macroscopically suspect tissue was compared to systematic sampling. Specimens of ileum, jejunum, mesenteric and caecal lymph nodes were examined for MAP infection using bacterial microscopy, culture, histopathology and immunohistochemistry. MAP was found most frequently in caecal lymph nodes, but sampling more tissues optimized the detection rate. Examination by culture was most efficient while combination with histopathology increased the detection rate slightly. MAP was detected in 49/50 animals with macroscopic lesions representing 1·35% of the slaughtered cattle examined. Of 150 systematically sampled macroscopically non-suspect cows, 28·7% were infected with MAP. This indicates that the majority of MAP-positive cattle are slaughtered without evidence of macroscopic lesions and before clinical signs occur. For reliable prevalence estimation of MAP infection in slaughtered cattle, systematic random sampling is essential.

scholarly journals tPRiors: A tool for prior elicitation and obtaining posterior distributions of true disease prevalence

2021 ◽  
Author(s):  
Konstantinos Pateras
Keyword(s):  
Bayesian Methods ◽  
Diagnostic Tests ◽  
False Negative ◽  
Disease Prevalence ◽  
Final Report ◽  
Prior Elicitation ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Prevalence Estimates ◽  
Multiple Population

Abstract Background: Tests have false positive or false negative results, which, if not properly accounted for, may provide misleading apparent prevalence estimates based on the observed rate of positive tests and not the true disease prevalence estimates. Methods to estimate the true prevalence of disease, adjusting for the sensitivity and the specificity of the diagnostic tests are available and can be applied, though, such procedures can be cumbersome to researchers with or without a solid statistical background.Objective: To create a web-based application that integrates statistical methods for Bayesian inference of true disease prevalence based on prior elicitation for the accuracy of the diagnostic tests. This tool allows practitioners to simultaneously analyse and visualize results while using interactive sliders and output prior/posterior plots.Methods: Three methods for prevalence prior elicitation and four core families of Bayesian methods have been combined and incorporated in this web tool. |tPRiors| user interface has been developed with R and Shiny and may be freely accessed on-line.Results: |tPRiors| allows researchers to use preloaded data or upload their own datasets and perform analysis on either single or multiple population groups clusters), allowing, if needed, for excess zero prevalence. The final report is exported in raw parts either as .rdata or .png files. We utilize a real multiple-population and a toy single-population dataset to demonstrate the robustness and capabilities of |tPRiors|.Conclusions: We expect |tPRiors| to be helpful for researchers interested in true disease prevalence estimation and they are keen on accounting for prior information. |tPRiors| acts both as a statistical tool and a simplified step-by-step statistical framework that facilitates the use of complex Bayesian methods. The application of |tPRiors| is expected to aid standardization of practices in the field of Bayesian modelling on subject and multiple group-based true prevalence estimation.

scholarly journals Frequentist and Bayesian approaches to prevalence estimation using examples from Johne's disease

2008 ◽  
Vol 9 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Locksley L. McV. Messam ◽  
Adam J. Branscum ◽  
Michael T. Collins ◽  
Ian A. Gardner
Keyword(s):  
Bayesian Methods ◽  
Direct Computation ◽  
Bayesian Approaches ◽  
Interval Estimates ◽  
True Prevalence ◽  
Prevalence Estimation ◽  
Point Estimates ◽  
Population Prevalence ◽  
Small Test ◽  
Pooled Samples

AbstractAlthough frequentist approaches to prevalence estimation are simple to apply, there are circumstances where it is difficult to satisfy assumptions of asymptotic normality and nonsensical point estimates (greater than 1 or less than 0) may result. This is particularly true when sample sizes are small, test prevalences are low and imperfect sensitivity and specificity of diagnostic tests need to be incorporated into calculations of true prevalence. Bayesian approaches offer several advantages including direct computation of range-respecting interval estimates (e.g. intervals between 0 and 1 for prevalence) without the requirement of transformations or large-sample approximations. They also allow direct probabilistic interpretation, and the flexibility to model in a straightforward manner the probability of zero prevalence. In this review, we present frequentist and Bayesian methods for animal- and herd-level true prevalence estimation based on individual and pooled samples. We provide statistical methods for detecting differences between population prevalence and frequentist methods for sample size and power calculations. All examples are motivated usingMycobacterium aviumsubspeciesparatuberculosisinfection and we provide WinBUGS code for all examples of Bayesian estimation.

scholarly journals Expanding Covid-19 Testing: Mathematical Guidelines for the Optimal Sample Pool Size Given Positive Test Rate

2020 ◽  
Author(s):  
Kayleigh Adams
Keyword(s):  
Statistical Modeling ◽  
Infection Rate ◽  
Pool Size ◽  
Positive Test ◽  
Optimal Sample ◽  
Sample Pooling ◽  
Explicit Bounds ◽  
Multiple Samples

AbstractWidespread testing is essential to the mitigation of the spread of any virus, and is particularly central to the discussion on transitioning out of national quarantine. Sample pooling is a method that aims to multiply testing capability by using one testing kit for multiple samples, but will only be successful under certain conditions. This paper gives precise guidelines on those conditions for success: for any proposed sample pool size, explicit bounds on the positive infection rate are given that are informed by both discrete and statistical modeling.

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