Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 - 2021/03) Using a Statistical Learning Strategy

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs [~]91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

A differential regulatory T cell signature distinguishes the immune landscape of COVID-19 hospitalized patients from those hospitalized with other respiratory viral infections

SARS-CoV-2 infection has caused a lasting global pandemic costing millions of lives and untold additional costs. Understanding the immune response to SARS-CoV-2 has been one of the main challenges in the past year in order to decipher mechanisms of host responses and interpret disease pathogenesis. Comparatively little is known in regard to how the immune response against SARS-CoV-2 differs from other respiratory infections. In our study, we compare the peripheral blood immune signature from SARS-CoV-2 infected patients to patients hospitalized pre-pandemic with Influenza Virus or Respiratory Syncytial Virus (RSV). Our in-depth profiling indicates that the immune landscape in patients infected by SARS-CoV-2 is largely similar to patients hospitalized with Flu or RSV. Similarly, serum cytokine and chemokine expression patterns were largely overlapping. Unique to patients infected with SARS-CoV-2 who had the most critical clinical disease state were changes in the regulatory T cell (Treg) compartment. A Treg signature including increased frequency, activation status, and migration markers was correlated with the severity of COVID-19 disease. These findings are particularly relevant as Tregs are being discussed as a therapy to combat the severe inflammation seen in COVID-19 patients. Likewise, having defined the overlapping immune landscapes in SARS-CoV-2, existing knowledge of Flu and RSV infections could be leveraged to identify common treatment strategies. HighlightsO_LIThe immune landscapes of hospitalized pre-pandemic RSV and influenza patients are similar to SARS-CoV-2 patients C_LIO_LISerum cytokine and chemokine expression patterns are largely similar between patients hospitalized with respiratory virus infections, including SARS-CoV-2, versus healthy donors C_LIO_LISARS-CoV-2 patients with the most critical disease displayed unique changes in the Treg compartment C_LIO_LIadvances in understanding and treating SARS-CoV-2 could be leveraged for other common respiratory infections C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/21254376v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1dddb4corg.highwire.dtl.DTLVardef@689a1corg.highwire.dtl.DTLVardef@15db5eaorg.highwire.dtl.DTLVardef@1521659_HPS_FORMAT_FIGEXP M_FIG C_FIG

Early super-spreader events are a likely determinant of novel SARS-CoV-2 variant predominance

The emergence of multiple new SARS-CoV-2 variants, characterized to varying degrees by increased infectivity, higher virulence and evasion of vaccine and infection-induced immunologic memory, has hampered international efforts to contain the virus. While it is generally believed that these variants first develop in single individuals with poor immunologic control of the virus, the factors governing variant predominance in the population remain poorly characterized. Here we present a mathematical framework for variant emergence accounting for the highly variable number of people secondarily infected by individuals with SARS-CoV-2 infection. Our simulations suggest that threatening new variants probably develop within infected people fairly commonly, but that most die out and do not achieve permanence in the population. Variants that predominate are more likely to be associated with higher infectiousness, but also the occurrence of a super-spreader event soon after introduction into the population.

Rapid vaccination and early reactive partial lockdown will minimize deaths from emerging highly contagious SARS-CoV-2 variants

The goals of SARS-CoV-2 vaccination programs are to maximally reduce cases and deaths, and to limit the amount of time required under lockdown. Using a mathematical model calibrated to data from King County Washington but generalizable across states, we simulated multiple scenarios with different vaccine efficacy profiles, vaccination rates, and case thresholds for triggering and relaxing partial lockdowns. We assumed that a contagious variant is currently present at low levels. In all scenarios, it rapidly becomes dominant by early summer. Low case thresholds for triggering partial lockdowns during current and future waves of infection strongly predict lower total numbers of COVID-19 infections, hospitalizations and deaths in 2021. However, in regions with relatively higher current seroprevalence, there is a predicted delay in onset of a subsequent surge in new variant infections. For all vaccine efficacy profiles, increasing vaccination rate lowers the total number of infections and deaths, as well as the total number of days under partial lockdown. Due to variable current estimates of emerging variant infectiousness, vaccine efficacy against these variants, vaccine refusal, and future adherence to masking and physical distancing, we project considerable uncertainty regarding the timing and intensity of subsequent waves of infection. Nevertheless, under all plausible scenarios, rapid vaccination and early implementation of partial lockdown are the two most critical variables to save the greatest number of lives.

Optimizing vaccine allocation for COVID-19 vaccines: critical role of single-dose vaccination.

Most COVID-19 vaccines require two doses, however with limited vaccine supply, policymakers are considering single-dose vaccination as an alternative strategy. Using a mathematical model combined with optimization algorithms, we determined optimal allocation strategies with one and two doses of vaccine under various degrees of viral transmission. Under low transmission, we show that the optimal allocation of vaccine vitally depends on the single-dose efficacy (SDE). With high SDE, single-dose vaccination is optimal, preventing up to 22% more deaths than a strategy prioritizing two-dose vaccination for older adults. With low or moderate SDE, mixed vaccination campaigns with complete coverage of older adults are optimal. However, with modest or high transmission, vaccinating older adults first with two doses is best, preventing up to 41% more deaths than a singledose vaccination given across all adult populations. Our work suggests that it is imperative to determine the efficacy and durability of single-dose vaccines, as mixed or single-dose vaccination campaigns may have the potential to contain the pandemic much more quickly.

Vaccines that prevent SARS-CoV-2 transmission may prevent or dampen a spring wave of COVID-19 cases and deaths in 2021

Ongoing SARS-CoV-2 vaccine trials assess vaccine efficacy against disease (VEDIS), the ability of a vaccine to block symptomatic COVID-19. They will only partially discriminate whether VEDIS is mediated by preventing infection as defined by the detection of virus in the airways (vaccine efficacy against infection defined as VESUSC), or by preventing symptoms despite breakthrough infection (vaccine efficacy against symptoms or VESYMP). Vaccine efficacy against infectiousness (VEINF), defined as the decrease in secondary transmissions from infected vaccine recipients versus from infected placebo recipients, is also not being measured. Using mathematical modeling of data from King County Washington, we demonstrate that if the Moderna and Pfizer vaccines, which have observed VEDIS>90%, mediate VEDIS predominately by complete protection against infection, then prevention of a fourth epidemic wave in the spring of 2021, and associated reduction of subsequent cases and deaths by 60%, is likely to occur assuming rapid enough vaccine roll out. If high VEDIS is explained primarily by reduction in symptoms, then VEINF>50% will be necessary to prevent or limit the extent of this fourth epidemic wave. The potential added benefits of high VEINF would be evident regardless of vaccine allocation strategy and would be enhanced if vaccine roll out rate is low or if available vaccines demonstrate waning immunity. Finally, we demonstrate that a 1.0 log vaccine-mediated reduction in average peak viral load might be sufficient to achieve VEINF=60% and that human challenge studies with 104 infected participants, or clinical trials in a university student population could estimate VESUSC, VESYMP and VEINF using viral load metrics.

Slight reduction in SARS-CoV-2 exposure viral load due to masking results in a significant reduction in transmission with widespread implementation

Masks are a vital tool for limiting SARS-CoV-2 spread in the population. Here we utilize a mathematical model to assess the impact of masking on transmission within individual transmission pairs and at the population level. Our model quantitatively links mask efficacy to reductions in viral load and subsequent transmission risk. Our results reinforce that the use of masks by both a potential transmitter and exposed person substantially reduces the probability of successful transmission, even if masks only lower exposure viral load by [~]50%. Slight increases in mask adherence and/or efficacy above current levels would reduce the effective reproductive number (Re) substantially below 1, particularly if implemented comprehensively in potential super-spreader environments. Our model predicts that moderately efficacious masks will lower exposure viral load 10-fold among people who get infected despite masking, potentially limiting infection severity. Because peak viral load tends to occur pre-symptomatically, we also identify that antiviral therapy targeting symptomatic individuals is unlikely to impact transmission risk. Instead, antiviral therapy would only lower Re if dosed as post-exposure prophylaxis and if given to [~]50% of newly infected people within 3 days of an exposure. These results highlight the primacy of masking relative to other biomedical interventions under consideration for limiting the extent of the COVID-19 pandemic prior to widespread implementation of a vaccine.

Widespread testing, case isolation and contact tracing may allow safe school reopening with continued moderate physical distancing: a modeling analysis of King County, WA data

BackgroundIn late March 2020, a "Stay Home, Stay Healthy" order was issued in Washington State in response to the COVID-19 pandemic. On May 1, a 4-phase reopening plan began. If implemented without interruptions, all types of public interactions were planned to resume by July 15. We investigated whether adjunctive prevention strategies would allow less restrictive physical distancing to avoid second epidemic waves and secure safe school reopening. MethodsWe developed a mathematical model, stratifying the population by age (0-19 years, 20-49 years, 50-69 years, and 70+ years), infection status (susceptible, exposed, asymptomatic, pre-symptomatic, symptomatic, recovered) and treatment status (undiagnosed, diagnosed, hospitalized) to project SARS-CoV-2 transmission during and after the reopening period. The model was parameterized with demographic and contact data from King County, WA and calibrated to confirmed cases, deaths (overall and by age) and epidemic peak timing. Adjunctive prevention interventions were simulated assuming different levels of pre-COVID physical interactions (pC_PI) restored. We made several predictions related to adjunctive interventions or changes in pC_PI. ResultsThe best model fit estimated ~35% pC_PI under lockdown. Gradually restoring 75% pC_PI for all age groups between May 15-July 15 resulted in ~350 daily deaths by early September 2020. Maintaining less than 45% pC_PI was required with current testing practices to ensure low levels of daily infections and deaths. If widespread community transmission persisted, isolating the elderly does not lower daily death rates significantly. Increased testing, isolation of symptomatic infections, and contact tracing permitted 60% pC_PI without significant increases in daily deaths before September, although this strategy may not be sufficient to eliminate community transmission. This combination strategy also allowed opening of schools with <15 daily deaths. Inpatient antiviral treatment reduces deaths significantly without lowering cases or hospitalizations. ConclusionsWe predict that widespread implementation of "test and isolate" policy alone is insufficient to prevent the rapid re-emergence of SARS CoV-2 without moderate physical distancing. However, widespread testing, contact tracing and case isolation would allow relaxation of physical distancing, as well as opening of schools, without a surge in local cases and deaths.

Wrong person, place and time: viral load and contact network structure predict SARS-CoV-2 transmission and super-spreading events

SARS-CoV-2 is difficult to contain because many transmissions occur during the pre-symptomatic phase of infection. Moreover, in contrast to influenza, while most SARS-CoV-2 infected people do not transmit the virus to anybody, a small percentage secondarily infect large numbers of people. We designed mathematical models of SARS-CoV-2 and influenza which link observed viral shedding patterns with key epidemiologic features of each virus, including distributions of the number of secondary cases attributed to each infected person (individual R0) and the duration between symptom onset in the transmitter and secondarily infected person (serial interval). We identify that people with SARS-CoV-2 or influenza infections are usually contagious for fewer than one day congruent with peak viral load several days after infection, and that transmission is unlikely below a certain viral load. SARS-CoV-2 super-spreader events with over 10 secondary infections occur when an infected person is briefly shedding at a very high viral load and has a high concurrent number of exposed contacts. The higher predisposition of SARS-CoV-2 towards super-spreading events is not due to its 1-2 additional weeks of viral shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks than a person infected with influenza, likely due to aerosolization of virus. Our results support policies that limit crowd size in indoor spaces and provide viral load benchmarks for infection control and therapeutic interventions intended to prevent secondary transmission. One Sentence SummaryWe developed a coupled within-host and between-host mathematical model to identify viral shedding levels required for transmission of SARS-CoV-2 and influenza, and to explain why super-spreading events occur more commonly during SARS-CoV-2 infection.

Mathematical modeling explains differential SARS CoV-2 kinetics in lung and nasal passages in remdesivir treated rhesus macaques

Remdesivir was recently demonstrated to decrease recovery time in hospitalized patients with SARS-CoV-2 infection. In rhesus macaques, early initiation of remdesivir therapy prevented pneumonia and lowered viral loads in the lung, but viral loads increased in the nasal passages five days after therapy. We developed mathematical models to explain these results. We identified that 1) drug potency is slightly higher in nasal passages than in lungs, 2) viral load decrease in lungs relative to nasal passages during therapy because of infection-dependent generation of refractory cells in the lung, 3) incomplete drug potency in the lung that decreases viral loads even slightly may allow substantially less lung damage, and 4) increases in nasal viral load may occur due to a slight blunting of peak viral load and subsequent decrease of the intensity of the innate immune response, as well as a lack of refractory cells. We also hypothesize that direct inoculation of the trachea in rhesus macaques may not recapitulate natural infection as lung damage occurs more abruptly in this model than in human infection. We demonstrate with sensitivity analysis that a drug with higher potency could completely suppress viral replication and lower viral loads abruptly in the nasal passages as well as the lung. One Sentence SummaryWe developed a mathematical model to explain why remdesivir has a greater antiviral effect on SARS CoV-2 in lung versus nasal passages in rhesus macaques.

Potency and timing of antiviral therapy as determinants of duration of SARS CoV-2 shedding and intensity of inflammatory response

Treatments are desperately needed to lower the hospitalization and case fatality rates of SARS CoV-2 infection. In order to meaningfully impact the COVID-19 pandemic, promising antiviral therapies must be identified within the next several months. However, the number of clinical trials that can be performed in this timeframe is limited. We therefore developed a mathematical model which allows projection of all possible therapeutic approaches. Our model recapitulates off-treatment viral dynamics and predicts a three-phase immune response. Addition of treatment with remdesivir, hydroxychloroquine, neutralizing antibodies or cellular immunotherapy demonstrates that if in vivo drug potency is high, then rapid elimination of virus is possible. Potent therapies dosed soon after peak viral load when infected people typically develop symptoms, are predicted to decrease shedding duration and intensity of the effector immune response, but to have little effect on viral area under the curve, which is driven by high levels of early SARS CoV-2 replication. Potent therapy dosed prior to peak viral load, when infection is usually pre-symptomatic, is predicted to be the only option to lower viral area under the curve. We also identify that clinically meaningful drug resistance is less likely to emerge with a highly potent agent that is dosed after peak viral load. Our results support an early test and treat approach for COVID-19, but also demonstrate the need to identify early viral shedding kinetic features that are the most predictive surrogates of clinical severity and transmission risk. One Sentence SummaryWe developed a mathematical model to predict the outcomes of different possible COVID-19 treatments.

Reliability of self-sampling for accurate assessment of respiratory virus viral and immunologic kinetics

The SARS-CoV-2 pandemic demonstrates the need for accurate and convenient approaches to diagnose and therapeutically monitor respiratory viral infections. We demonstrated that self-sampling with foam swabs is well-tolerated and provides quantitative viral output concordant with flocked swabs. Using longitudinal home-based self-sampling, we demonstrate nasal cytokine levels correlate and cluster according to immune cell of origin. Periods of stable viral loads are followed by rapid elimination, which could be coupled with cytokine expansion and contraction using mathematical models. Nasal foam swab self-sampling at home provides a precise, mechanistic readout of respiratory virus shedding and local immune responses.