Vaccines Alone Cannot Slow the Evolution of SARS-CoV-2.

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.

Heterogeneity in Vaccinal Immunity to SARS-CoV-2 Can Be Addressed by a Personalized Booster Strategy.

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.

Reproducible Science Is Vital for a Stronger Evidence Base During the COVID-19 Pandemic.

Reproducible research becomes even more imperative as we build the evidence base on SARS-CoV-2 epidemiology, diagnosis, prevention, and treatment. In his study, Paez assessed the reproducibility of COVID-19 research during the pandemic, using a case study of population density. He found that most articles that assess the relationship of population density and COVID-19 outcomes do not publicly share data and code, except for a few, including our paper, which he stated "illustrates the importance of good reproducibility practices". Paez recreated our analysis using our code and data from the perspective of spatial analysis, and his new model came to a different conclusion. The disparity between our and Paez's findings, as well as other existing literature on the topic, give greater impetus to the need for further research. As there has been near exponential growth of COVID-19 research across a wide range of scientific disciplines, reproducible science is a vital component to produce reliable, rigorous, and robust evidence on COVID-19, which will be essential to inform clinical practice and policy in order to effectively eliminate the pandemic.

Early introduction and rise of the Omicron SARS-CoV-2 variant in highly vaccinated university populations.

BACKGROUND: The Omicron variant of SARS-CoV-2 is highly transmissible in vaccinated and unvaccinated populations. The dynamics governing its establishment and propensity towards fixation (reaching 100% frequency in the SARS-CoV-2 population) in communities remain unknown. In this work, we describe the dynamics of Omicron at three institutions of higher education (IHEs) in the greater Boston area. METHODS: We use diagnostic and variant-specifying molecular assays and epidemiological analytical approaches to describe the rapid dominance of Omicron following its introduction to three IHEs with asymptomatic surveillance programs. RESULTS: We show that the establishment of Omicron at IHEs precedes that of the state and region, and that the time to fixation is shorter at IHEs (9.5-12.5 days) than in the state (14.8 days) or region. We show that the trajectory of Omicron fixation among university employees resembles that of students, with a 2-3 day delay. Finally, we compare cycle threshold (Ct) values in Omicron vs. Delta variant cases on college campuses, and identify lower viral loads among college affiliates harboring Omicron infections. CONCLUSIONS: We document the rapid takeover of the Omicron variant at IHEs, reaching near-fixation within the span of 9.5-12.5 days despite lower viral loads, on average, than the previously dominant Delta variant. These findings highlight the transmissibility of Omicron, its propensity to rapidly dominate small populations, and the ability of robust asymptomatic surveillance programs to offer early insights into the dynamics of pathogen arrival and spread.

Viral dynamics of Omicron and Delta SARS-CoV-2 variants with implications for timing of release from isolation: a longitudinal cohort study.

BACKGROUND: In January 2022, United States guidelines shifted to recommend isolation for 5 days from symptom onset, followed by 5 days of mask wearing. However, viral dynamics and variant and vaccination impact on culture conversion are largely unknown. METHODS: We conducted a longitudinal study on a university campus, collecting daily anterior nasal swabs for at least 10 days for RT-PCR and culture, with antigen rapid diagnostic testing (RDT) on a subset. We compared culture positivity beyond day 5, time to culture conversion, and cycle threshold trend when calculated from diagnostic test, from symptom onset, by SARS-CoV-2 variant, and by vaccination status. We evaluated sensitivity and specificity of RDT on days 4-6 compared to culture. RESULTS: Among 92 SARS-CoV-2 RT-PCR positive participants, all completed the initial vaccine series, 17 (18.5%) were infected with Delta and 75 (81.5%) with Omicron. Seventeen percent of participants had positive cultures beyond day 5 from symptom onset with the latest on day 12. There was no difference in time to culture conversion by variant or vaccination status. For 14 sub-study participants, sensitivity and specificity of day 4-6 RDT were 100% and 86% respectively. CONCLUSIONS: The majority of our Delta- and Omicron-infected cohort culture-converted by day 6, with no further impact of booster vaccination on sterilization or cycle threshold decay. We found that rapid antigen testing may provide reassurance of lack of infectiousness, though guidance to mask for days 6-10 is supported by our finding that 17% of participants remained culture positive after isolation.

No magic bullet: Limiting in-school transmission in the face of variable SARS-CoV-2 viral loads.

In the face of a long-running pandemic, understanding the drivers of ongoing SARS-CoV-2 transmission is crucial for the rational management of COVID-19 disease burden. Keeping schools open has emerged as a vital societal imperative during the pandemic, but in-school transmission of SARS-CoV-2 can contribute to further prolonging the pandemic. In this context, the role of schools in driving SARS-CoV-2 transmission acquires critical importance. Here we model in-school transmission from first principles to investigate the effectiveness of layered mitigation strategies on limiting in-school spread. We examined the effect of masks and air quality (ventilation, filtration and ionizers) on steady-state viral load in classrooms, as well as on the number of particles inhaled by an uninfected person. The effectiveness of these measures in limiting viral transmission was assessed for variants with different levels of mean viral load (ancestral, Delta, Omicron). Our results suggest that a layered mitigation strategy can be used effectively to limit in-school transmission, with certain limitations. First, poorly designed strategies (insufficient ventilation, no masks, staying open under high levels of community transmission) will permit in-school spread even if some level of mitigation is present. Second, for viral variants that are sufficiently contagious, it may be difficult to construct any set of interventions capable of blocking transmission once an infected individual is present, underscoring the importance of other measures. Our findings provide practical recommendations; in particular, the use of a layered mitigation strategy that is designed to limit transmission, with other measures such as frequent surveillance testing and smaller class sizes (such as by offering remote schooling options to those who prefer it) as needed.

Endemicity Is Not a Victory: The Unmitigated Downside Risks of Widespread SARS-CoV-2 Transmission

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.

Estimation of heterogeneous instantaneous reproduction numbers with application to characterize SARS-CoV-2 transmission in Massachusetts counties.

The reproductive number is an important metric that has been widely used to quantify the infectiousness of communicable diseases. The time-varying instantaneous reproductive number is useful for monitoring the real-time dynamics of a disease to inform policy making for disease control. Local estimation of this metric, for instance at a county or city level, allows for more targeted interventions to curb transmission. However, simultaneous estimation of local reproductive numbers must account for potential sources of heterogeneity in these time-varying quantities-a key element of which is human mobility. We develop a statistical method that incorporates human mobility between multiple regions for estimating region-specific instantaneous reproductive numbers. The model also can account for exogenous cases imported from outside of the regions of interest. We propose two approaches to estimate the reproductive numbers, with mobility data used to adjust incidence in the first approach and to inform a formal priori distribution in the second (Bayesian) approach. Through a simulation study, we show that region-specific reproductive numbers can be well estimated if human mobility is reasonably well approximated by available data. We use this approach to estimate the instantaneous reproductive numbers of COVID-19 for 14 counties in Massachusetts using CDC case report data and the human mobility data collected by SafeGraph. We found that, accounting for mobility, our method produces estimates of reproductive numbers that are distinct across counties. In contrast, independent estimation of county-level reproductive numbers tends to produce similar values, as trends in county case-counts for the state are fairly concordant. These approaches can also be used to estimate any heterogeneity in transmission, for instance, age-dependent instantaneous reproductive number estimates. As people are more mobile and interact frequently in ways that permit transmission, it is important to account for this in the estimation of the reproductive number.

Standardising care for children admitted with Acute Wheeze: implementation of a new discharge bundle in a major Urban Hospital

1261 Figure 1Acute wheeze discharge proforma[Figure omitted. See PDF] 1261 Figure 2Discharge practices/post -intervention*Respiratory team includes medical and cns members[Figure omitted. See PDF]Results28 children were admitted in November 2020 with acute wheeze, and 37 children were admitted post-intervention in November 2021. Results pre & post-intervention are summarised in figure 2. Post-intervention, approx. 40% more patients were reviewed by the asthma team in hospital, and approx. 60% more children were provided with a written personalised asthma plan. The first audit cycle identified variable practice in advice given regarding salbutamol use at discharge. Local guidance was changed in line with GINA recommendations,5 and post-intervention, the use of salbutamol weaning regimes fell by ~85%.ConclusionImplementation of a standardised discharge bundle for acute wheeze improved compliance with BTS discharge guidance, in all areas measured, with the caveat that pre-intervention practice may have been influenced by the Covid-19 pandemic. Our expectation that this significant increase in clinical workload will translate into better outcomes (e.g., reduced exacerbation/admission rates, improved symptom control), and we plan to examine this in future.ReferencesState of Child Health, RCPCH, 2020.Why asthma still kills: the National Review of Asthma Deaths (NRAD) Confidential Enquiry report. RCP, 2014.BTS Asthma Discharge Care Bundle: 2016, British Thoracic Society, 2016.BTS/SIGN Guideline for the Management of Asthma 2019, BTS/SIGN, 2019.Global Strategy for Asthma Management and Prevention, Global Initiative for Asthma, 2019.

Projected Long-Term Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on Hepatitis C Outcomes in the United States: A Modeling Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic disrupted access to and uptake of hepatitis C virus (HCV) care services in the United States. It is unknown how substantially the pandemic will impact long-term HCV-related outcomes. METHODS: We used a microsimulation to estimate the 10-year impact of COVID-19 disruptions in healthcare delivery on HCV outcomes including identified infections, linkage to care, treatment initiation and completion, cirrhosis, and liver-related death. We modeled hypothetical scenarios consisting of an 18-month pandemic-related disruption in HCV care starting in March 2020 followed by varying returns to pre-pandemic rates of screening, linkage, and treatment through March 2030 and compared them to a counterfactual scenario in which there was no COVID-19 pandemic or disruptions in care. We also performed alternate scenario analyses in which the pandemic disruption lasted for 12 and 24 months. RESULTS: Compared to the "no pandemic" scenario, in the scenario in which there is no return to pre-pandemic levels of HCV care delivery, we estimate 1060 fewer identified cases, 21 additional cases of cirrhosis, and 16 additional liver-related deaths per 100 000 people. Only 3% of identified cases initiate treatment and <1% achieve sustained virologic response (SVR). Compared to "no pandemic," the best-case scenario in which an 18-month care disruption is followed by a return to pre-pandemic levels, we estimated a smaller proportion of infections identified and achieving SVR. CONCLUSIONS: A recommitment to the HCV epidemic in the United States that involves additional resources coupled with aggressive efforts to screen, link, and treat people with HCV is needed to overcome the COVID-19-related disruptions.

Estimation of local time-varying reproduction numbers in noisy surveillance data.

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Comparison of anterior nares CT values in asymptomatic and symptomatic individuals diagnosed with SARS-CoV-2 in a university screening program.

At our university based high throughput screening program, we test all members of our community weekly using RT-qPCR. RT-qPCR cycle threshold (CT) values are inversely proportional to the amount of viral RNA in a sample and are a proxy for viral load. We hypothesized that CT values would be higher, and thus the viral loads at the time of diagnosis would be lower, in individuals who were infected with the virus but remained asymptomatic throughout the course of the infection. We collected the N1 and N2 target gene CT values from 1633 SARS-CoV-2 positive RT-qPCR tests of individuals sampled between August 7, 2020, and March 18, 2021, at the BU Clinical Testing Laboratory. We matched this data with symptom reporting data from our clinical team. We found that asymptomatic patients had CT values significantly higher than symptomatic individuals on the day of diagnosis. Symptoms were followed by the clinical team for 10 days post the first positive test. Within the entire population, 78.1% experienced at least one symptom during surveillance by the clinical team (n = 1276/1633). Of those experiencing symptoms, the most common symptoms were nasal congestion (73%, n = 932/1276), cough (60.0%, n = 761/1276), fatigue (59.0%, n = 753/1276), and sore throat (53.1%, n = 678/1276). The least common symptoms were diarrhea (12.5%, n = 160/1276), dyspnea on exertion (DOE) (6.9%, n = 88/1276), foot or skin changes (including rash) (4.2%, n = 53/1276), and vomiting (2.1%, n = 27/1276). Presymptomatic individuals, those who were not symptomatic on the day of diagnosis but became symptomatic over the following 10 days, had CT values higher for both N1 (median = 27.1, IQR 20.2-32.9) and N2 (median = 26.6, IQR 20.1-32.8) than the symptomatic group N1 (median = 21.8, IQR 17.2-29.4) and N2 (median = 21.4, IQR 17.3-28.9) but lower than the asymptomatic group N1 (median = 29.9, IQR 23.6-35.5) and N2 (median = 30.0, IQR 23.1-35.7). This study supports the hypothesis that viral load in the anterior nares on the day of diagnosis is a measure of disease intensity at that time.

The role of schools in driving SARS-CoV-2 transmission: Not just an open-and-shut case.

Keeping schools open without permitting COVID-19 spread has been complicated by conflicting messages around the role of children and schools in fueling the pandemic. Here, we describe methodological limitations of research minimizing SARS-CoV-2 transmission in schools, and we review evidence for safely operating schools while reducing overall SARS-CoV-2 transmission.

Vertebral artery dissection managed by interventional radiology.

A 21-year-old patient presented with sudden-onset headache, visual disturbance and left hand incoordination. She was diagnosed with a left vertebral artery dissection of the V3 segment resulting in multiple cerebellar and cerebral infarcts. There were no risk factors for dissection other than recent COVID-19 infection. She was treated initially with antiplatelets, followed by anticoagulation, but experienced recurrent ischaemia. Although guidance suggests endovascular repair may be beneficial for patients with cerebral artery dissection (CAD) who experience recurrent strokes on medical therapy, evidence is limited. After multidisciplinary team consideration of the individual patient anatomy and risks and benefits of different endovascular techniques, the patient was treated with endovascular coiling. At 10 months follow-up, she had no further strokes and improving neurological symptoms. The case highlighted COVID-19 as a potential trigger for CAD and the use of endovascular coiling in preventing catastrophic cerebral ischaemia in CAD refractive to medical therapy.

Reproducible Science Is Vital for a Stronger Evidence Base During the COVID‐19 Pandemic

Reproducible research becomes even more imperative as we build the evidence base on SARS‐CoV‐2 epidemiology, diagnosis, prevention, and treatment. In his study, Paez assessed the reproducibility of COVID‐19 research during the pandemic, using a case study of population density. He found that most articles that assess the relationship of population density and COVID‐19 outcomes do not publicly share data and code, except for a few, including our paper, which he stated “illustrates the importance of good reproducibility practices”. Paez recreated our analysis using our code and data from the perspective of spatial analysis, and his new model came to a different conclusion. The disparity between our and Paez’s findings, as well as other existing literature on the topic, give greater impetus to the need for further research. As there has been near exponential growth of COVID‐19 research across a wide range of scientific disciplines, reproducible science is a vital component to produce reliable, rigorous, and robust evidence on COVID‐19, which will be essential to inform clinical practice and policy in order to effectively eliminate the pandemic.

Statistical Estimation of the Reproductive Number From Case Notification Data.

The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modeling expertise to be implemented. In this article, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally, we conclude with a discussion of available software and opportunities for future development.

Spectrum, risk factors and outcomes of neurological and psychiatric complications of COVID-19: a UK-wide cross-sectional surveillance study.

SARS-CoV-2 is associated with new-onset neurological and psychiatric conditions. Detailed clinical data, including factors associated with recovery, are lacking, hampering prediction modelling and targeted therapeutic interventions. In a UK-wide cross-sectional surveillance study of adult hospitalized patients during the first COVID-19 wave, with multi-professional input from general and sub-specialty neurologists, psychiatrists, stroke physicians, and intensivists, we captured detailed data on demographics, risk factors, pre-COVID-19 Rockwood frailty score, comorbidities, neurological presentation and outcome. A priori clinical case definitions were used, with cross-specialty independent adjudication for discrepant cases. Multivariable logistic regression was performed using demographic and clinical variables, to determine the factors associated with outcome. A total of 267 cases were included. Cerebrovascular events were most frequently reported (131, 49%), followed by other central disorders (95, 36%) including delirium (28, 11%), central inflammatory (25, 9%), psychiatric (25, 9%), and other encephalopathies (17, 7%), including a severe encephalopathy (n = 13) not meeting delirium criteria; and peripheral nerve disorders (41, 15%). Those with the severe encephalopathy, in comparison to delirium, were younger, had higher rates of admission to intensive care and a longer duration of ventilation. Compared to normative data during the equivalent time period prior to the pandemic, cases of stroke in association with COVID-19 were younger and had a greater number of conventional, modifiable cerebrovascular risk factors. Twenty-seven per cent of strokes occurred in patients <60 years. Relative to those >60 years old, the younger stroke patients presented with delayed onset from respiratory symptoms, higher rates of multi-vessel occlusion (31%) and systemic thrombotic events. Clinical outcomes varied between disease groups, with cerebrovascular disease conferring the worst prognosis, but this effect was less marked than the pre-morbid factors of older age and a higher pre-COVID-19 frailty score, and a high admission white cell count, which were independently associated with a poor outcome. In summary, this study describes the spectrum of neurological and psychiatric conditions associated with COVID-19. In addition, we identify a severe COVID-19 encephalopathy atypical for delirium, and a phenotype of COVID-19 associated stroke in younger adults with a tendency for multiple infarcts and systemic thromboses. These clinical data will be useful to inform mechanistic studies and stratification of patients in clinical trials.

Beyond the new normal: Assessing the feasibility of vaccine-based suppression of SARS-CoV-2.

As the COVID-19 pandemic drags into its second year, there is hope on the horizon, in the form of SARS-CoV-2 vaccines which promise disease suppression and a return to pre-pandemic normalcy. In this study we critically examine the basis for that hope, using an epidemiological modeling framework to establish the link between vaccine characteristics and effectiveness in bringing an end to this unprecedented public health crisis. Our findings suggest that a return to pre-pandemic social and economic conditions without fully suppressing SARS-CoV-2 will lead to extensive viral spread, resulting in a high disease burden even in the presence of vaccines that reduce risk of infection and mortality. Our modeling points to the feasibility of complete SARS-CoV-2 suppression with high population-level compliance and vaccines that are highly effective at reducing SARS-CoV-2 infection. Notably, vaccine-mediated reduction of transmission is critical for viral suppression, and in order for partially-effective vaccines to play a positive role in SARS-CoV-2 suppression, complementary biomedical interventions and public health measures must be deployed simultaneously.

Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic.

Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve.