A Universal Day Zero Infectious Disease Testing Strategy Leveraging CRISPR-based Sample Depletion and Metagenomic Sequencing (preprint)

The lack of preparedness for detecting the highly infectious SARS-CoV-2 pathogen, the pathogen responsible for the COVID-19 disease, has caused enormous harm to public health and the economy. It took ~60 days for the first reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests for SARS-CoV-2 infection developed by the United States Centers for Disease Control (CDC) to be made publicly available. It then took >270 days to deploy 800,000 of these tests at a time when the estimated actual testing needs required over 6 million tests per day. Testing was therefore limited to individuals with symptoms or in close contact with confirmed positive cases. Testing strategies deployed on a population scale at "Day Zero" i.e., at the time of the first reported case, would be of significant value. Next Generation Sequencing (NGS) has such Day Zero capabilities with the potential for broad and large-scale testing. However, it has limited detection sensitivity for low copy numbers of pathogens which may be present. Here we demonstrate that by using CRISPR-Cas9 to remove abundant sequences that do not contribute to pathogen detection, NGS detection sensitivity of COVID-19 is comparable to RT-qPCR. In addition, we show that this assay can be used for variant strain typing, co-infection detection, and individual human host response assessment, all in a single workflow using existing open-source analysis pipelines. This NGS workflow is pathogen agnostic, and therefore has the potential to transform how both large-scale pandemic response and focused clinical infectious disease testing are pursued in the future.

Efficient prevalence estimation and infected sample identification with group testing for SARS-CoV-2 (preprint)

The ongoing pandemic of SARS-CoV-2, a novel coronavirus, caused over 3 million reported cases of coronavirus disease 2019 (COVID-19) and 200,000 reported deaths between December 2019 and April 2020. Cases and deaths will increase as the virus continues its global march outward. In the absence of effective pharmaceutical interventions or a vaccine, wide-spread virological screening is required to inform where restrictive isolation measures should be targeted and when they can be lifted. However, limitations on testing capacity have restricted the ability of governments and institutions to identify individual clinical cases, appropriately measure community prevalence, and mitigate transmission. Group testing offers a way to increase efficiency, by combining samples and testing a small number of pools. Here, we evaluate the effectiveness of group testing designs for individual identification or prevalence estimation of SARS-CoV-2 infection when testing capacity is limited. To do this, we developed mathematical models for epidemic spread, incorporating empirically measured individual-level viral kinetics to simulate changing viral loads in a large population over the course of an epidemic. We used these to construct representative populations and assess pooling strategies for community screening, accounting for variability in viral load samples, dilution effects, changing prevalence and resource constraints. We confirmed our group testing framework through pooled tests on de-identified human nasopharyngeal specimens with viral loads representative of the larger population. We show that group testing designs can both accurately estimate overall prevalence using a small number of measurements and substantially increase the identification rate of infected individuals in resource-limited settings.