Using viral load and epidemic dynamics to optimize pooled testing in resource constrained settings

Modeling for COVID-19 college reopening decisions: Cornell, a case study

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities' unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University's decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

Pooling for SARS-CoV2 Surveillance: Validation and Strategy for Implementation in K-12 Schools

Repeated testing of a population is critical for limiting the spread of the SARS-CoV-2 virus and for the safe reopening of educational institutions such as kindergarten-grade 12 (K-12) schools and colleges. Many screening efforts utilize the CDC RT-PCR based assay which targets two regions of the novel Coronavirus nucleocapsid gene. The standard approach of testing each person individually, however, poses a financial burden to these institutions and is therefore a barrier to using testing for re-opening. Pooling samples from multiple individuals into a single test is an attractive alternate approach that promises significant cost savings-however the specificity and sensitivity of such approaches needs to be assessed prior to deployment. To this end, we conducted a pilot study to evaluate the feasibility of analyzing samples in pools of eight by the established RT-PCR assay. Participants (1,576) were recruited from amongst the Tufts University community undergoing regular screening. Each volunteer provided two swabs, one analyzed separately and the other in a pool of eight. Because the positivity rate was very low, we spiked approximately half of the pools with laboratory-generated swabs produced from known positive cases outside the Tufts testing program. The results of pooled tests had 100% correspondence with those of their respective individual tests. We conclude that pooling eight samples does not negatively impact the specificity or sensitivity of the RT-PCR assay and suggest that this approach can be utilized by institutions seeking to reduce surveillance costs.

Sample pooling as a strategy for community monitoring for SARS-CoV-2

Sample pooling strategy was intended to determine the optimal parameters for group testing of pooled specimens for the detection of SARS-CoV-2 and process them without significant loss of test usability. Standard molecular diagnostic laboratory equipment, and commercially available centrifugal filters, RNA isolation kits and SARS Cov2 PCR tests were used. The basic idea was to combine and concentrate several samples to the maximal volume, which can be extracted with the single extraction column. Out of 16 tested pools, 12 were positive with cycle threshold (Ct) values within 0.5 and 3.01 Ct of the original individual specimens. The analysis of 112 specimens determined that 12 pools were positive, followed by identification of 6 positive individual specimens among the 112 tested. This testing was accomplished with the use of 16 extractions/PCR tests, resulting in saving of 96 reactions but adding the 40 centrifugal filters. The present study demonstrated that pool testing could detect even up to a single positive sample with Ct value as high as 34. According to the standard protocols, reagents and equipment, this pooling method can be applied easily in current clinical testing laboratories.

Fundamental limitations of contact tracing for COVID-19

Contact tracing has played a central role in COVID-19 control in many jurisdictions and is often used in conjunction with other measures such as travel restrictions and social distancing mandates. Contact tracing is made ineffective, however, by delays in testing, calling, and isolating. Even if delays are minimized, contact tracing triggered by testing of symptomatic individuals can only prevent a fraction of onward transmissions from contacts. Without other measures in place, contact tracing alone is insufficient to prevent exponential growth in the number of cases in a population with little immunity. Even when used effectively with other measures, occasional bursts in call loads can overwhelm contact tracing systems and lead to a loss of control. We propose embracing approaches to COVID-19 contact tracing that broadly test individuals without symptoms, in whatever way is economically feasible—either with fast and cheap tests that can be deployed widely, with pooled testing, or with screening of judiciously chosen groups of high-risk individuals. These considerations are important both in regions where widespread vaccination has been deployed and in those where few residents have been immunized.

Assessment of an Online Tool to Simulate the Effect of Pooled Testing for SARS-CoV-2 Detection in Asymptomatic and Symptomatic Populations

This diagnostic study describes an online tool created with actual severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus copy number data to help policy makers understand how pooled testing compares with single-sample testing in different populations.

Pooling of coronavirus tests under unknown prevalence

Diagnostic testing for the novel coronavirus is an important tool to fight the coronavirus disease (Covid-19) pandemic. However, testing capacities are limited. A modified testing protocol, whereby a number of probes are 'pooled' (i.e. grouped), is known to increase the capacity for testing. Here, we model pooled testing with a double-average model, which we think to be close to reality for Covid-19 testing. The optimal pool size and the effect of test errors are considered. The results show that the best pool size is three to five, under reasonable assumptions. Pool testing even reduces the number of false positives in the absence of dilution effects.