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 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 Deletions of pfhrp2 and pfhrp3 genes were uncommon in rapid diagnostic test-negative Plasmodium falciparum isolates from Uganda

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Sam L. Nsobya ◽  
Andrew Walakira ◽  
Elizabeth Namirembe ◽  
Moses Kiggundu ◽  
Joaniter I. Nankabirwa ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Expression ◽  
Diagnostic Tests ◽  
False Negative ◽  
Pcr Amplification ◽  
Rapid Diagnostic Tests ◽  
False Negatives ◽  
Cross Sectional ◽  
True Prevalence ◽  
Blood Smears

Abstract Background Rapid diagnostic tests (RDTs) play a key role in malaria case management. The most widely used RDT identifies Plasmodium falciparum based on immunochromatographic recognition of P. falciparum histidine-rich protein 2 (PfHRP2). Deletion of the paralogous pfhrp2 and pfhrp3 genes leads to false-negative PfHRP2-based RDTs, and has been reported in P. falciparum infections from South America and Africa. However, identification of pfhrp2/pfhrp3 deletions has usually been based only on failure to amplify these genes using PCR, without confirmation based on PfHRP2 protein expression, and understanding of the true prevalence of deletions is incomplete. Methods Deletions of pfhrp2/pfhrp3 in blood samples were investigated from cross-sectional surveys in 2012-13 in three regions of varied malaria transmission intensity in Uganda. Samples with positive Giemsa-stained thick blood smears, but negative PfHRP2-based RDTs were evaluated by PCR amplification of conserved subunit ribosomal DNA for Plasmodium species, PCR amplification of pfhrp2 and pfhrp3 genes to identify deletions, and bead-based immunoassays for expression of PfHRP2. Results Of 3516 samples collected in cross-sectional surveys, 1493 (42.5%) had positive blood smears, of which 96 (6.4%) were RDT-negative. Of these 96 RDT-negative samples, P. falciparum DNA was identified by PCR in 56 (58%) and only non-falciparum plasmodial DNA in 40 (42%). In all 56 P. falciparum-positive samples there was a failure to amplify pfhrp2 or pfhrp3: in 25 (45%) pfhrp2 was not amplified, in 39 (70%) pfhrp3 was not amplified, and in 19 (34%) neither gene was amplified. For the 39 P. falciparum-positive, RDT-negative samples available for analysis of protein expression, PfHRP2 was not identified by immunoassay in only four samples (10.3%); these four samples all had failure to amplify both pfhrp2 and pfhrp3 by PCR. Thus, only four of 96 (4.2%) smear-positive, RDT-negative samples had P. falciparum infections with deletion of pfhrp2 and pfhrp3 confirmed by failure to amplify the genes by PCR and lack of expression of PfHRP2 demonstrated by immunoassay. Conclusion False negative RDTs were uncommon. Deletions in pfhrp2 and pfhrp3 explained some of these false negatives, but most false negatives were not due to deletion of the pfhrp2 and pfhrp3 genes.

scholarly journals Occupancy modeling and resampling overcomes low test sensitivity to produce accurate SARS-CoV-2 prevalence estimates

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jamie S. Sanderlin ◽  
Jessie D. Golding ◽  
Taylor Wilcox ◽  
Daniel H. Mason ◽  
Kevin S. McKelvey ◽  
...  
Keyword(s):  
Low Cost ◽  
False Negative ◽  
Emerging Infectious Diseases ◽  
Disease Prevalence ◽  
Occupancy Modeling ◽  
Test Sensitivity ◽  
Prevalence Estimates ◽  
Repeated Sampling ◽  
Rapid Tests ◽  
Low Sensitivity

Abstract Background We evaluated whether occupancy modeling, an approach developed for detecting rare wildlife species, could overcome inherent accuracy limitations associated with rapid disease tests to generate fast, accurate, and affordable SARS-CoV-2 prevalence estimates. Occupancy modeling uses repeated sampling to estimate probability of false negative results, like those linked to rapid tests, for generating unbiased prevalence estimates. Methods We developed a simulation study to estimate SARS-CoV-2 prevalence using rapid, low-sensitivity, low-cost tests and slower, high-sensitivity, higher cost tests across a range of disease prevalence and sampling strategies. Results Occupancy modeling overcame the low sensitivity of rapid tests to generate prevalence estimates comparable to more accurate, slower tests. Moreover, minimal repeated sampling was required to offset low test sensitivity at low disease prevalence (0.1%), when rapid testing is most critical for informing disease management. Conclusions Occupancy modeling enables the use of rapid tests to provide accurate, affordable, real-time estimates of the prevalence of emerging infectious diseases like SARS-CoV-2.

scholarly journals Deletions of pfhrp2 and pfhrp3 genes were uncommon in rapid diagnostic test-negative Plasmodium falciparum isolates from Uganda

2020 ◽  
Author(s):  
Sam L Nsobya ◽  
Andrew Walakira ◽  
Elizabeth Namirembe ◽  
Moses Kiggundu ◽  
Joaniter I Nankabirwa ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Expression ◽  
Diagnostic Tests ◽  
False Negative ◽  
Pcr Amplification ◽  
Rapid Diagnostic Tests ◽  
False Negatives ◽  
Cross Sectional ◽  
True Prevalence ◽  
Blood Smears

Abstract BackgroundRapid diagnostic tests (RDTs) play a key role in malaria case management. The most widely used RDT identifies Plasmodium falciparum based on immunochromatographic recognition of P. falciparum histidine-rich protein 2 (PfHRP2). Deletion of the paralogous pfhrp2 and pfhrp3 genes leads to false-negative PfHRP2-based RDTs, and has been reported in P. falciparum infections from South America and Africa. However, identification of pfhrp2/pfhrp3 deletions has usually been based only on failure to amplify these genes using PCR, without confirmation based on PfHRP2 protein expression, and understanding of the true prevalence of deletions is incomplete.MethodsDeletions of pfhrp2/pfhrp3 in blood samples were investigated from cross-sectional surveys in 2012-13 in three regions of varied malaria transmission intensity in Uganda. Samples with positive Giemsa-stained thick blood smears, but negative PfHRP2-based RDTs were evaluated by PCR amplification of conserved subunit ribosomal DNA for Plasmodium species, PCR amplification of pfhrp2 and pfhrp3 genes to identify deletions, and bead-based immunoassays for expression of PfHRP2.ResultsOf 3516 samples collected in cross-sectional surveys, 1493 (42.5%) had positive blood smears, of which 96 (6.4%) were RDT-negative. Of these 96 RDT-negative samples, P. falciparum DNA was identified by PCR in 56 (58%) and only non-falciparum plasmodial DNA in 40 (42%). In all 56 P. falciparum-positive samples there was a failure to amplify pfhrp2 or pfhrp3: in 25 (45%) pfhrp2 was not amplified, in 39 (70%) pfhrp3 was not amplified, and in 19 (34%) neither gene was amplified. For the 39 P. falciparum-positive, RDT-negative samples available for analysis of protein expression, PfHRP2 was not identified by immunoassay in only four samples (10.3%); these four samples all had failure to amplify both pfhrp2 and pfhrp3 by PCR. Thus, only four of 96 (4.2%) smear-positive, RDT-negative samples had P. falciparum infections with deletion of pfhrp2 and pfhrp3 confirmed by failure to amplify the genes by PCR and lack of expression of PfHRP2 demonstrated by immunoassay.ConclusionFalse negative RDTs were uncommon. Deletions in pfhrp2 and pfhrp3 explained some of these false negatives, but most false negatives were not due to deletion of the pfhrp2 and pfhrp3 genes.

An Epidemiologic Critique of Current Microbial Risk Assessment Practices: The Importance of Prevalence and Test Accuracy Data

2004 ◽  
Vol 67 (9) ◽  
pp. 2000-2007 ◽  
Author(s):  
IAN A. GARDNER
Keyword(s):  
Risk Assessment ◽  
Bayesian Methods ◽  
Gold Standard ◽  
Latent Class ◽  
Test Accuracy ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
True Prevalence ◽  
Apparent Prevalence ◽  
Prevalence Estimates

Data deficiencies are impeding the development and validation of microbial risk assessment models. One such deficiency is the failure to adjust test-based (apparent) prevalence estimates to true prevalence estimates by correcting for the imperfect accuracy of tests that are used. Such adjustments will facilitate comparability of data from different populations and from the same population over time as tests change and the unbiased quantification of effects of mitigation strategies. True prevalence can be estimated from apparent prevalence using frequentist and Bayesian methods, but the latter are more flexible and can incorporate uncertainty in test accuracy and prior prevalence data. Both approaches can be used for single or multiple populations, but the Bayesian approach can better deal with clustered data, inferences for rare events, and uncertainty in multiple variables. Examples of prevalence inferences based on results of Salmonella culture are presented. The opportunity to adjust test-based prevalence estimates is predicated on the availability of sensitivity and specificity estimates. These estimates can be obtained from studies using archived gold standard (reference) samples, by screening with the new test and follow-up of test-positive and test-negative samples with a gold standard test, and by use of latent class methods, which make no assumptions about the true status of each sampling unit. Latent class analysis can be done with maximum likelihood and Bayesian methods, and an example of their use in the evaluation of tests for Toxoplasma gondii in pigs is presented. Guidelines are proposed for more transparent incorporation of test data into microbial risk assessments.

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 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.

scholarly journals Mapping chronic disease prevalence based on medication use and socio-demographic variables: an application of LASSO on administrative data sources in healthcare in the Netherlands

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Koen Füssenich ◽  
Hendriek C. Boshuizen ◽  
Markus M. J. Nielen ◽  
Erik Buskens ◽  
Talitha L. Feenstra
Keyword(s):  
Chronic Disease ◽  
Hospital Admission ◽  
Medication Use ◽  
Disease Prevalence ◽  
Percentage Error ◽  
Economic Variables ◽  
Regional Patterns ◽  
Prevalence Estimates ◽  
Socio Economic Variables ◽  
Chronic Disease Prevalence

Abstract Background Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease. Methods Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant. Results Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age. Conclusion Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy.

An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier

2008 ◽  
Author(s):  
Brian Bucci ◽  
Jeffrey Vipperman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Methods ◽  
False Positive ◽  
Impulse Noise ◽  
False Negative ◽  
Training Data ◽  
Maximum Accuracy ◽  
First Case ◽  
Testing Data

In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural network structures, Bayesian methods are investigated to classify the same dataset. Four interesting cases are identified and analyzed: A) Maximum accuracy achieve on training data, B) Maximum overall accuracy on blind testing data, C) Maximum accuracy on testing data with zero false positive detections, D) Maximum accuracy on testing data with zero false negative rejections. The first case is used to illustrative example and the later three represent actual monitoring modes. All of the cases are compared and contrasted to illuminate respective strengths and weaknesses. Overall accuracies of up to 99.8% are observed with no false negative rejections and accuracies of up to 98.4% are also achieved with no false positive detections.

Sign in / Sign up

Export Citation Format

Share Document