HYPER: Group testing via hypergraph factorization applied to COVID-19 (preprint)

Large scale screening is a critical tool in the life sciences, but is often limited by reagents, samples, or cost. An important challenge in screening has recently manifested in the ongoing effort to achieve widespread testing for individuals with SARS-CoV-2 infection in the face of substantial resource constraints. Group testing methods utilize constrained testing resources more efficiently by pooling specimens together, potentially allowing larger populations to be screened with fewer tests. A key challenge in group testing is to design an effective pooling strategy. The global nature of the ongoing pandemic calls for something simple (to aid implementation) and flexible (to tailor for settings with differing needs) that remains efficient. Here we propose HYPER, a new group testing method based on hypergraph factorizations. We provide theoretical characterizations under a general statistical model, and exhaustively evaluate HYPER and proposed alternatives for SARS-CoV-2 screening under realistic simulations of epidemic spread and within-host viral kinetics. We demonstrate that HYPER performs at least as well as other methods in scenarios that are well-suited to each method, while outperforming those methods across a broad range of resource-constrained environments, being more flexible and simple in design, and taking no expertise to implement. An online tool to implement these designs in the lab is available at http://hyper.covid19-analysis.org .

Maternal Outcomes and Risk Factors for Severity Among Pregnant Women With COVID-19: A Case Control Study From the COVI-Preg International Registry (preprint)

Background: Recent evidence suggests that pregnant women might be at higher risk of severe disease associated with the emerging pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while exposed fetuses/newborns could suffer from preterm birth, growth restriction and neonatal infections. The magnitude of this increased risk and specific risk factors for severity remains unclear.Methods: We performed a case control study comparing pregnant women with severe coronavirus disease 19 (case) to pregnant women with a milder form (controls) enrolled in COVI-Preg international registry cohort between from March 24 to July 26, 2020. Risk factors for severity, obstetrical, fetal and neonatal outcomes were assessed.Findings: A total of 926 pregnant women with a positive test for SARS-CoV-2 were included, among which 92 (9.9%) presented a severe COVID-19 disease. Risk factors for severe maternal outcomes were pulmonary comorbidities [aOR 4.3, 95% CI 1.9-9.5], hypertensive disorders [aOR 2.7, 95% CI 1.0-7.0] and diabetes [aOR2.2, 95% CI 1.1-4.5]. Pregnant women with severe maternal outcomes were at higher risk of cesarean sections [70.7% (n=53/75)], preterm deliveries [62.7% (n= 32/51)] and newborns requiring admission to the neonatal intensive care unit [41.3% (n=31/75)].Interpretation: Pregnant women, particularly those with associated comorbidities, seem to be at higher risk of severe complications of SARS-CoV-2 infection. Obstetrical and neonatal outcomes appear to be influenced by the severity of maternal disease; complications include cesarean sections, prematurity and neonatal admission to the intensive care unit.Funding Statement: None.Declaration of Interests: The authors declare that they have no conflicts of interest.Ethics Approval Statement: The study was approved by both the Swiss Ethical Board (CER-VD- 2020-00548) and the local ethics boards at each participating center.

Maternal and Obstetrical Outcomes in a Cohort of Pregnant Women Tested for SARS-CoV-2: Interim Results of the COVI-Preg International Registry (preprint)

Background: Pregnant women represent a vulnerable population at higher risk of complications of infectious diseases. Data regarding the consequences of the emerging pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during pregnancy are scarce. Recent evidence suggests that pregnant women might be at higher risk of severe disease, while exposed fetuses and newborns could suffer from preterm birth, growth restriction and neonatal infections.Methods: We developed an international web registry to allow structured data collection. Pregnant women at any stage during gestation tested for SARS-CoV-2 infection were enrolled. Maternal, obstetrical and neonatal outcomes were recorded.Findings: 1033 pregnant women tested for SARS-CoV-2 were included, among which 926 tested positive and 107 tested negative. Positive pregnant women were at higher risk of severe maternal outcomes compared to negative women [aRR 5.6, 95% CI 1.4-22.7]. Risk factors for severe maternal outcomes among positive women were pulmonary comorbidities [aOR 4.3, 95% CI 1.9-9.5], hypertensive disorders [aOR 2.7, 95% CI 1.0-7.0] and diabetes [aOR2.2, 95% CI 1.1-4.5]. No difference in term of obstetrical and neonatal outcomes were observed between positive and negative women. Positive pregnant women with severe maternal outcomes were at higher risk of cesarean sections [70.7% (n=53/75)], preterm deliveries [62.7% (n= 32/51)] and newborns requiring admission to the neonatal intensive care unit [41.3% (n=31/75)]. A positive neonatal SARS-CoV-2 test was observed in 2.9% (n=11/384) of newborns with an available test at birth.Interpretation: Pregnant women, particularly those with associated comorbidities, seem to be at higher risk of severe complications of SARS-CoV-2 infection. Preliminary data regarding obstetrical and neonatal outcomes among women with a mild disease are reassuring.Funding Statement: None.Declaration of Interests: The authors declare that we have no conflicts of interest.Ethics Approval Statement: The study was approved by both the Swiss Ethical Board (CER-VD-2020-00548) and the local ethics boards at each participating center.

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.

LAMP-Seq: Population-Scale COVID-19 Diagnostics Using a Compressed Barcode Space (preprint)

The ongoing SARS-CoV-2 pandemic has already caused devastating losses. Exponential spread can be slowed by social distancing and population-wide isolation measures, but those place a tremendous burden on society, and, once lifted, exponential spread can re-emerge. Regular population-scale testing, combined with contact tracing and case isolation, should help break the cycle of transmission, but current detection strategies are not capable of such large-scale processing. Here we present a protocol for LAMP-Seq, a barcoded Reverse-Transcription Loop-mediated Isothermal Amplification (RT-LAMP) method that is highly scalable. Individual samples are stabilized, inactivated, and amplified in three isothermal heat steps, generating barcoded amplicons that can be pooled and analyzed en masse by sequencing. Using unique barcode combinations per sample from a compressed barcode space enables extensive pooling, potentially further reducing cost and simplifying logistics. We validated LAMP-Seq on 28 clinical samples, empirically optimized the protocol and barcode design, and performed initial safety evaluation. Relying on world-wide infrastructure for next-generation sequencing, and in the context of population-wide sample collection, LAMP-Seq could be scaled to analyze millions of samples per day.