covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies.

BACKGROUND: COVID-19 has caused over 305 million infections and nearly 5.5 million deaths globally. With complete eradication unlikely, organizations will need to evaluate their risk and the benefits of mitigation strategies, including the effects of regular asymptomatic testing. We developed a web application and R package that provides estimates and visualizations to aid the assessment of organizational infection risk and testing benefits to facilitate decision-making, which combines internal and community information with malleable assumptions. RESULTS: Our web application, covidscreen, presents estimated values of risk metrics in an intuitive graphical format. It shows the current expected number of active, primarily community-acquired infections among employees in an organization. It calculates and explains the absolute and relative risk reduction of an intervention, relative to the baseline scenario, and shows the value of testing vaccinated and unvaccinated employees. In addition, the web interface allows users to profile risk over a chosen range of input values. The performance and output are illustrated using simulations and a real-world example from the employee testing program of a pediatric oncology specialty hospital. CONCLUSIONS: As the COVID-19 pandemic continues to evolve, covidscreen can assist organizations in making informed decisions about whether to incorporate covid test based screening as part of their on-campus risk-mitigation strategy. The web application, R package, and source code are freely available online (see "Availability of data and materials").

Hybrid Mathematical Model for Data Classification and Prediction: Case study COVID-19

The increase of data availability has stimulated researchers to benefit from this data in predicting the hidden pattern for knowledge discovery. Data classification and machine learning algorithms are becoming important tools used in knowledge discovery. In this paper, we propose a hybrid classification model that combines some features and parameters from a probabilistic model and some other parameters from a divide and conquer model in a linear one. In our model, we generate a set of functions related to the number of attributes and the value of each attribute. Afterwards, these functions are reduced according to the number of classes needed. We test our model on collected data about symptoms in people infected with COVID-19 in England. Our simulation results show an accuracy rate in the range 50-80%. We expect to increase the accuracy rate if we increase the size of data used or we increase the number of attributes. © 2022. All Rights Reserved.

An accurate model for SARS-CoV-2 pooled RT-PCR test errors.

Pooling is a method of simultaneously testing multiple samples for the presence of pathogens. Pooling of SARS-CoV-2 tests is increasing in popularity, due to its high testing throughput. A popular pooling scheme is Dorfman pooling: test N individuals simultaneously, if the test is positive, each individual is then tested separately; otherwise, all are declared negative. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel and parsimonious probabilistic model for the outcomes of pooled tests. As an application, we analyse the false-negative rate (i.e. the probability of a negative result for an infected individual) of Dorfman pooling. We show that the false-negative rates under Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition when Dorfman pooling achieves highest throughput efficiency. We therefore urge the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests' error rates.

Estimation of Undetected Asymptomatic COVID-19 Cases in South Korea Using a Probabilistic Model.

Increasing evidence shows that many infections of COVID-19 are asymptomatic, becoming a global challenge, since asymptomatic infections have the same infectivity as symptomatic infections. We developed a probabilistic model for estimating the proportion of undetected asymptomatic COVID-19 patients in the country. We considered two scenarios: one is conservative and the other is nonconservative. By combining the above two scenarios, we gave an interval estimation of 0.0001-0.0027 and in terms of the population, 5200-139,900 is the number of undetected asymptomatic cases in South Korea as of 2 February 2021. In addition, we provide estimates for total cases of COVID-19 in South Korea. Combination of undetected asymptomatic cases and undetected symptomatic cases to the number of confirmed cases (78,844 cases on 2 February 2021) shows that 0.17-0.42% (89,244-218,744) of the population have COVID-19. In conclusion, to control and understand the true ongoing reality of the pandemic, it is of outermost importance to focus on the ratio of undetected asymptomatic cases in the total population.