This app is a tool for identifying groups of people who are likely to be infected with SARS-CoV-2. Applied to public health, the output can inform the development of an efficient SARS-CoV-2 testing strategy. In a research setting, the output can help researchers more efficiently recruit people with SARS-CoV-2 for studies.
To use this app, start with a dataset that includes one record per individual in the target population (see definitions below for more). Note that to use this app, SARS-CoV-2 test results from a subset or census of the target population must be available, and the individuals who were tested must have been tested without regard to presentation of symptoms or contact with infected individuals. The previously tested individuals make up the study sample. You may also download a CSV of dummy data.
To see how to use, FAQs and definitions, click here.
The purpose of this app is to calculate the optimal pool size of hierarchical or square matrix group testing. We have included three different pooling algorithms in our app, enabling users to compare various methods.
Not all SARS-CoV-2 tests are created equal. This app is designed to calculate the true prevalence of SARS-CoV-2 given an original study population and SARS-CoV-2 test used to identify infection. Understanding the true prevalence can improve interpretation of study generalizability and limitations.
The ability to rapidly identify people likely to be infected with SARS-CoV-2 both speeds infection control efforts and results in better-powered clinical trials and observational studies. This app is a tool for identifying groups of people likely to be infected with SARS-CoV-2, including asymptomatic and pre-symptomatic cases. The purpose of the app is to help institutional decision-makers and researchers compare SARS-CoV-2 testing strategies to determine who in a given population should be approached for testing or recruited for a study on SARS-CoV-2.
Laboratories that are interested in conducting group testing for COVID-19 should use this app to find the optimal pool size. Anyone interested in understanding the usage of group testing should use this app for reference.
Anyone! This app may be used by researchers and the general public to understand SARS-CoV-2 prevalence better from study data and to understand the impact test accuracy has in understanding the penetrance of SARS-CoV-2 in our community.
This app may be used by decision-makers wishing to optimize transmission control effects through testing in settings where universal testing is not feasible. The method implemented by this app can apply to a variety of institutional settings, workplaces, or schools. In addition, it is often impractical to test every single individual in a randomized or observational study of SARS-CoV-2. Therefore, this app is also useful for researchers wishing to optimize statistical power in any study where people at higher risk of infection must be preferentially sampled.
At minimum, you need an estimate of SARS-CoV-2 prevalence in a population and the name of a SARS-CoV-2 test that is used to obtain that estimate of prevalence. Additionally, you can incorporate information about the precision of the result if you have confidence intervals for the estimated study prevalence. If the study you’re examining has a test not listed in our app, you may use the calculator by providing estimates of the sensitivity and specificity of the SARS-CoV-2 test used in the study.
This app assumes that some testing has already been performed in the target population, either as part of a specific data collection effort (e.g. a random sample of employees at a company were tested in a study on workplace exposure to SARS-CoV-2) or a universal testing activity (e.g. at the beginning of the semester, all students at a university were tested for SARS-CoV-2). Use of the app requires data on characteristics that could be used to propose testing strategies that would prioritize individuals with certain characteristics for testing. For example, data from housing contracts can be used to estimate the testing efficiency and yield of a strategy that prioritizes on-campus students for SARS-CoV-2 testing.
This app is designed to be used with either of the following two types of datasets:
For more information about dataset file structure, click to the "How to use" tab above.
From this app, one can find the optimal pool size for the different pooling algorithms available. Users can compare and contrast the optimal pool size, efficiency, positive predicted values, and total sensitivity of each pooling algorithm with varying prevalence. Furthermore, one can visualize the relationship between prevalence and optimal pool size, and prevalence and efficiency for different algorithms.
This app is a tool for re-calculating the prevalence of SARS-CoV-2 in studies based on the accuracy of the test used. The output can help inform the true prevalence or seroprevalence of SARS-CoV-2. Additionally, the calculator can help educate on how the accuracy of SARS-CoV-2 tests affects our understanding of the penetrance of SARS-CoV-2 in different communities.
This app can help decision-makers evaluate candidate SARS-CoV-2 testing strategies in terms of their efficiency and yield. Comparing the efficiency and yield of potential testing strategies can help stakeholders make informed decisions about which testing strategy or strategies to pursue, given available resources.
If you do not input an estimated prevalence or study population, the app will not be able to compute an adjusted prevalence and corresponding confidence interval. If the estimated prevalence is too low for a given study population, the calculator may not be able to calculate an adjusted prevalence if the sensitivity and specificity is low enough. Finally, some tests have sparse cells for the validation data, which may produce an error. You can check the validation data in the testing tab to diagnose if this is the case. Feel free to contact us if you have additional questions.
This app currently has only one viral dynamic model as from (Pilcher, Westreich, Hudgens; JID 2020).
The efficiency, adjusted sensitivity (due to larger pool size), and positive predictive value outputs should be regarded as conservative estimates. The optimal pool size output should be regarded as approximate. Finally, we assume that if a sample with a given viral load can be detected in the master pool, it can be detected in any smaller pools.
1. Upload a dataset that includes one record per individual in the target population.
The dataset should include: (1) the test results of individuals in the study sample, (2) sampling weight and clustering variables for individuals in the study sample, if applicable, and (3) a binary indicator variable for each candidate strategy that identifies whether each individual in the target population would or would not be selected for testing under each candidate strategy.
2. Specify variables on the left, including strategies for comparison.
3. App will calculate and display the following for each candidate testing strategy (more information is provided on these terms in the FAQ section):
The total number of COVID-19 cases that have been identified by a specific testing strategy.
The number of individuals in the target population who would need to be tested using a given strategy to find, in expectation, one case of SARS-CoV-2.
The proportion of SARS-CoV-2-positive cases identified out of all individuals tested.
The group of individuals who have been offered SARS-Cov-2 testing.
The group of all individuals who may be eligible for SARS-CoV-2 (COVID-19) testing under a specific testing strategy.