ABSTRACT
Throughout the current SARS-CoV-2 pandemic, limited diagnostic testing capacity prevented sentinel testing of the population, demonstrating the need for novel testing strategies and infrastructures. Here, we describe the set-up of an alternative testing platform, which allows scalable surveillance testing as an acute pandemic response tool and for pandemic preparedness purposes, exemplified by SARS-CoV-2 diagnostics in an academic environment. The testing strategy involves self-sampling based on gargling saline, pseudonymized sample handling, automated 96-well plate-based RNA extraction, and viral RNA detection using a semi-quantitative multiplexed colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay with an analytical sensitivity comparable to RT-quantitative polymerase chain reaction (RT-qPCR). We provide standard operating procedures and an integrated software solution for all workflows, including sample logistics, LAMP assay analysis by colorimetry or by sequencing (LAMP-seq), and communication of results to participants and the health authorities. Using large sample sets including longitudinal sample series we evaluated factors affecting the viral load and the stability of gargling samples as well as the diagnostic sensitivity of the RT-LAMP assay. We performed >35,000 tests during the pandemic, with an average turnover time of fewer than 6 hours from sample arrival at the test station to result announcement. Altogether, our work provides a blueprint for fast, sensitive, scalable, cost- and labor-efficient RT-LAMP diagnostics. As RT-LAMP-based testing requires advanced, but non-specialized laboratory equipment, it is independent of potentially limiting clinical diagnostics supply chains. One-sentence summaryA blueprint for scalable RT-LAMP test capacity for the sensitive detection of viral genomes demonstrated by SARS-CoV-2 surveillance testing.
ABSTRACT
Information is the most potent protective weapon we have to combat a pandemic, at both the individual and global level. For individuals, information can help us make personal decisions and provide a sense of security. For the global community, information can inform policy decisions and offer critical insights into the epidemic of COVID-19 disease. Fully leveraging the power of information, however, requires large amounts of data and access to it. To achieve this, we are making steps to form an international consortium, Coronavirus Census Collective (CCC, coronaviruscensuscollective.org), that will serve as a hub for integrating information from multiple data sources that can be utilized to understand, monitor, predict, and combat global pandemics. These sources may include self-reported health status through surveys (including mobile apps), results of diagnostic laboratory tests, and other static and real-time geospatial data. This collective effort to track and share information will be invaluable in predicting hotspots of disease outbreak, identifying which factors control the rate of spreading, informing immediate policy decisions, evaluating the effectiveness of measures taken by health organizations on pandemic control, and providing critical insight on the etiology of COVID-19. It will also help individuals stay informed on this rapidly evolving situation and contribute to other global efforts to slow the spread of disease. In the past few weeks, several initiatives across the globe have surfaced to use daily self-reported symptoms as a means to track disease spread, predict outbreak locations, guide population measures and help in the allocation of healthcare resources. The aim of this paper is to put out a call to standardize these efforts and spark a collaborative effort to maximize the global gain while protecting participant privacy.
