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Abstract Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2’s propensity for asymptomatic transmission, raise the question “how reliable was contact tracing for COVID-19 in the United States”? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens. 

Introduction:A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods:Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest (“mimotopes”) usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion:Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods. 

As the COVID-19 pandemic progresses, widespread community transmission of SARS-CoV-2 has ushered in a volatile era of viral immune evasion rather than the much-heralded stability of “endemicity” or “herd immunity.” At this point, an array of viral strains has rendered essentially all monoclonal antibody therapeutics obsolete and strongly undermined the impact of vaccinal immunity on SARS-CoV-2 transmission. In this work, we demonstrate that antibody escape resulting in evasion of pre-existing immunity is highly evolutionarily favored and likely to cause waves of short-term transmission. In the long-term, invading strains that induce weak cross-immunity against pre-existing strains may co-circulate with those pre-existing strains. This would result in the formation of serotypes that increase disease burden, complicate SARS-CoV-2 control, and raise the potential for increases in viral virulence. Less durable immunity does not drive positive selection as a trait, but such strains may transmit at high levels if they establish. Overall, our results draw attention to the importance of inter-strain cross-immunity as a driver of transmission trends and the importance of early immune evasion data to predict the trajectory of the pandemic. 

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral “spike” proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the six families above provides sufficient information to identify the protein(s) responsible for viral attachment. Random forest models that take as input a set of respiratory viral sequences can classify the protein as “spike” vs. non-spike based on predicted secondary structure elements alone (with 97.3% correctly classified) or in combination with N-glycosylation related features (with 97.0% correctly classified). Models were validated through 10-fold cross-validation, bootstrapping on a class-balanced set, and an out-of-sample extra-familial validation set. Surprisingly, we showed that secondary structural elements and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics. Furthermore, this approach may be extendable for the identification of other potential viral targets and for viral sequence annotation in general in the future. 

SARS-CoV-2 vaccinations were initially shown to substantially reduce risk of severe disease and death. However, pharmacokinetic (PK) waning and rapid viral evolution degrade neutralizing antibody (nAb) binding titers, causing loss of vaccinal protection. Additionally, there is inter-individual heterogeneity in the strength and durability of the vaccinal nAb response. Here, we propose a personalized booster strategy as a potential solution to this problem. Our model-based approach incorporates inter-individual heterogeneity in nAb response to primary SARS-CoV-2 vaccination into a pharmacokinetic/pharmacodynamic (PK/PD) model to project population-level heterogeneity in vaccinal protection. We further examine the impact of evolutionary immune evasion on vaccinal protection over time based on variant fold reduction in nAb potency. Our findings suggest viral evolution will decrease the effectiveness of vaccinal protection against severe disease, especially for individuals with a less durable immune response. More frequent boosting may restore vaccinal protection for individuals with a weaker immune response. Our analysis shows that the ECLIA RBD binding assay strongly predicts neutralization of sequence-matched pseudoviruses. This may be a useful tool for rapidly assessing individual immune protection. Our work suggests vaccinal protection against severe disease is not assured and identifies a potential path forward for reducing risk to immunologically vulnerable individuals. 

The rapid emergence of immune-evading viral variants of SARS-CoV-2 calls into question the practicality of a vaccine-only public-health strategy for managing the ongoing COVID-19 pandemic. It has been suggested that widespread vaccination is necessary to prevent the emergence of future immune-evading mutants. Here, we examined that proposition using stochastic computational models of viral transmission and mutation. Specifically, we looked at the likelihood of emergence of immune escape variants requiring multiple mutations and the impact of vaccination on this process. Our results suggest that the transmission rate of intermediate SARS-CoV-2 mutants will impact the rate at which novel immune-evading variants appear. While vaccination can lower the rate at which new variants appear, other interventions that reduce transmission can also have the same effect. Crucially, relying solely on widespread and repeated vaccination (vaccinating the entire population multiple times a year) is not sufficient to prevent the emergence of novel immune-evading strains, if transmission rates remain high within the population. Thus, vaccines alone are incapable of slowing the pace of evolution of immune evasion, and vaccinal protection against severe and fatal outcomes for COVID-19 patients is therefore not assured. 

The nasopharynx, at the back of the nose, constitutes the dominant initial viral infection trigger zone along the upper respiratory tract. However, as per the standard recommended usage protocol (“Current Use”, or CU) for intranasal sprays, the nozzle should enter the nose almost vertically, resulting in sub-optimal nasopharyngeal drug deposition. Through the Large Eddy Simulation technique, this study has replicated airflow under standard breathing conditions with 15 and 30 L/min inhalation rates, passing through medical scan-based anatomically accurate human airway cavities. The small-scale airflow fluctuations were resolved through use of a sub-grid scale Kinetic Energy Transport Model. Intranasally sprayed droplet trajectories for different spray axis placement and orientation conditions were subsequently tracked via Lagrangian-based inert discrete phase simulations against the ambient inhaled airflow field. Finally, this study verified the computational projections for the upper airway drug deposition trends against representative physical experiments on sprayed delivery performed in a 3D-printed anatomic replica. The model-based exercise has revealed a new “Improved Use” (or, IU) spray usage protocol for viral infections. It entails pointing the spray bottle at a shallower angle (with an almost horizontal placement at the nostril), aiming slightly toward the cheeks. From the conically injected spray droplet simulations, we have summarily derived the following inferences: (a) droplets sized between 7–17  μ m are relatively more efficient at directly reaching the nasopharynx via inhaled transport; and (b) with realistic droplet size distributions, as found in current over-the-counter spray products, the targeted drug delivery through the IU protocol outperforms CU by a remarkable 2 orders-of-magnitude. 

The strategy of relying solely on current SARS-CoV-2 vaccines to halt SARS-CoV-2 transmission has proven infeasible. In response, many public-health authorities have advocated for using vaccines to limit mortality while permitting unchecked SARS-CoV-2 spread (“learning to live with the disease”). The feasibility of this strategy critically depends on the infection fatality rate (IFR) of SARS-CoV-2. An expectation exists that the IFR will decrease due to selection against virulence. In this work, we perform a viral fitness estimation to examine the basis for this expectation. Our findings suggest large increases in virulence for SARS-CoV-2 would result in minimal loss of transmissibility, implying that the IFR may vary freely under neutral evolutionary drift. We use an SEIRS model framework to examine the effect of hypothetical changes in the IFR on steady-state death tolls under COVID-19 endemicity. Our modeling suggests that endemic SARS-CoV-2 implies vast transmission resulting in yearly US COVID-19 death tolls numbering in the hundreds of thousands under many plausible scenarios, with even modest increases in the IFR leading to unsustainable mortality burdens. Our findings highlight the importance of enacting a concerted strategy and continued development of biomedical interventions to suppress SARS-CoV-2 transmission and slow its evolution.