ABSTRACT
As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.
ABSTRACT
Remdesivir (RDV, GS-5734) is currently the only FDA-approved antiviral drug for the treatment of SARS CoV-2 infection. The drug is approved for use in adults or children 12-years or older who are hospitalized for the treatment of COVID-19 on the basis of an acceleration of clinical recovery for inpatients with this disease. Unfortunately, the drug must be administered intravenously, restricting its use to those requiring hospitalization for relatively advanced disease. RDV is also unstable in plasma and has a complex activation pathway which may contribute to its highly variable antiviral efficacy in SARS-CoV-2 infected cells. Potent orally bioavailable antiviral drugs for early treatment of SARS-CoV-2 infection are urgently needed and several including molnupiravir and PF-07321332 are currently in clinical development. We focused on making simple, orally bioavailable lipid analogs of Remdesivir nucleoside (RVn, GS-441524) that are processed to RVn-monophosphate, the precursor of the active RVn-triphosphate, by a single-step intracellular cleavage. In addition to high oral bioavailability, stability in plasma and simpler metabolic activation, new oral lipid prodrugs of RVn had submicromolar anti-SARS-CoV-2 activity in a variety of cell types including Vero E6, Calu-3, Caco-2, human pluripotent stem cell (PSC)-derived lung cells and Huh7.5 cells. In Syrian hamsters oral treatment with ODBG-P-RVn was well tolerated and achieved therapeutic levels in plasma above the EC90 for SARS-CoV-2. The results suggest further evaluation as an early oral treatment for SARS-CoV-2 infection to minimize severe disease and reduce hospitalizations.
ABSTRACT
BackgroundEarly detection and risk mitigation efforts are essential for averting large outbreaks of SARS-CoV-2. Active surveillance for SARS-CoV-2 can aid in early detection of outbreaks, but the testing frequency required to identify an outbreak at its earliest stage is unknown. We assess what testing frequency is required to detect an outbreak before there are 10 detectable infections. MethodsA dynamic compartmental transmission model of SARS-CoV-2 was developed to simulate spread among a university community. After introducing a single infection into a fully susceptible population, we calculate the probability of detecting at least one case on each succeeding day with various NAT testing frequencies (daily testing achieving 25%, 50%, 75%, and 100% of the population tested per month) assuming an 85% test sensitivity. A proportion of infected individuals (varied from 1-60%) are assumed to present to health services (HS) for symptomatic testing. We ascertain the expected number of detectable infections in the community when there is a > 90% probability of detecting at least 1 case. Sensitivity analyses examine impact of transmission rates (Rt = 0 = 2, 2.5,3), presentation to HS (1%/5%/30%/60%), and pre-existing immunity (0%/10%) ResultsAssuming an 85% test sensitivity, identifying an outbreak with 90% probability when the expected number of detectable infections is 9 or fewer requires NAT testing of 100% of the population per month; this result holds for all transmission rates and all levels of presentation at health services we considered. If 1% of infected people present at HS and Rt=0=3, testing 75%/50%/25% per month could identify an outbreak when the expected numbers of detectable infections are 12/17/30 respectively; these numbers decline to 9/11/12 if 30% of infected people present at HS. As proportion of infected individuals present at health services increases, the marginal impact of active surveillance is reduced. Higher transmission rates result in shorter time to detection but also rapidly escalating cases without intervention. Little differences were observed with 10% pre-existing immunity. ConclusionsWidespread testing of 100% of the campus population every month is required to detect an outbreak when there are fewer than 9 detectable infections for the scenarios examined, but high presentation of symptomatic people at HS can compensate in part for lower levels of testing. Early detection is necessary, but not sufficient, to curtail disease outbreaks; the proposed testing rates would need to be accompanied by case isolation, contact tracing, quarantine, and other risk mitigation and social distancing interventions.
