Covid-19 vaccine effectiveness against general SARS-CoV-2 infection from the omicron variant: A retrospective cohort study

SARS-CoV-2 variant introduction following spring break travel and transmission mitigation strategies

BACKGROUND

University spring break carries a two-pronged SARS-CoV-2 variant transmission risk. Circulating variants from universities can spread to spring break destinations, and variants from spring break destinations can spread to universities and surrounding communities. Therefore, it is critical to implement SARS-CoV-2 variant surveillance and testing strategies to limit community spread before and after spring break to mitigate virus transmission and facilitate universities safely returning to in-person teaching.

METHODS

We examined the SARS-CoV-2 positivity rate and changes in variant lineages before and after the university spring break for two consecutive years. 155 samples were sequenced across four time periods: pre- and post-spring break 2021 and pre- and post-spring break 2022; following whole genome sequencing, samples were assigned clades. The clades were then paired with positivity and testing data from over 50,000 samples.

RESULTS

In 2021, the number of variants in the observed population increased from four to nine over spring break, with variants of concern being responsible for most of the cases; Alpha percent composition increased from 22.2% to 56.4%. In 2022, the number of clades in the population increased only from two to three, all of which were Omicron or a sub-lineage of Omicron. However, phylogenetic analysis showed the emergence of distantly related sub-lineages. 2022 saw a greater increase in positivity than 2021, which coincided with a milder mitigation strategy. Analysis of social media data provided insight into student travel destinations and how those travel events may have impacted spread.

CONCLUSIONS

We show the role that repetitive testing can play in transmission mitigation, reducing community spread, and maintaining in-person education. We identified that distantly related lineages were brought to the area after spring break travel regardless of the presence of a dominant variant of concern.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Utilizing a university testing program to estimate relative effectiveness of monovalent COVID-19 mRNA booster vaccine versus two-dose primary series against symptomatic SARS-CoV-2 infection

Vaccine effectiveness (VE) studies utilizing the test-negative design are typically conducted in clinical settings, rather than community populations, leading to bias in VE estimates against mild disease and limited information on VE in healthy young adults. In a community-based university population, we utilized data from a large SARS-CoV-2 testing program to estimate relative VE of COVID-19 mRNA vaccine primary series and monovalent booster dose versus primary series only against symptomatic SARS-CoV-2 infection from September 2021 to July 2022. We used the test-negative design and logistic regression implemented via generalized estimating equations adjusted for age, calendar time, prior SARS-CoV-2 infection, and testing frequency (proxy for test-seeking behavior) to estimate relative VE. Analyses included 2,218 test-positive cases (59 % received monovalent booster dose) and 9,615 test-negative controls (62 %) from 9,066 individuals, with median age of 21 years, mostly students (71 %), White (56 %) or Asian (28 %), and with few comorbidities (3 %). More cases (23 %) than controls (6 %) had COVID-19-like illness. Estimated adjusted relative VE of primary series and monovalent booster dose versus primary series only against symptomatic SARS-CoV-2 infection was 40 % (95 % CI: 33-47 %) during the overall analysis period and 46 % (39-52 %) during the period of Omicron circulation. Relative VE was greater for those without versus those with prior SARS-CoV-2 infection (41 %, 34-48 % versus 33 %, 9 %-52 %, P < 0.001). Relative VE was also greater in the six months after receiving a booster dose (41 %, 33-47 %) compared to more than six months (27 %, 8-42 %), but this difference was not statistically significant (P = 0.06). In this relatively young and healthy adult population, an mRNA monovalent booster dose provided increased protection against symptomatic SARS-CoV-2 infection, overall and with the Omicron variant. University testing programs may be utilized for estimating VE in healthy young adults, a population that is not well-represented by routine VE studies.

Preclinical immune efficacy against SARS-CoV-2 beta B.1.351 variant by MVA-based vaccine candidates

The constant appearance of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VoCs) has jeopardized the protective capacity of approved vaccines against coronavirus disease-19 (COVID-19). For this reason, the generation of new vaccine candidates adapted to the emerging VoCs is of special importance. Here, we developed an optimized COVID-19 vaccine candidate using the modified vaccinia virus Ankara (MVA) vector to express a full-length prefusion-stabilized SARS-CoV-2 spike (S) protein, containing 3 proline (3P) substitutions in the S protein derived from the beta (B.1.351) variant, termed MVA-S(3Pbeta). Preclinical evaluation of MVA-S(3Pbeta) in head-to-head comparison to the previously generated MVA-S(3P) vaccine candidate, expressing a full-length prefusion-stabilized Wuhan S protein (with also 3P substitutions), demonstrated that two intramuscular doses of both vaccine candidates fully protected transgenic K18-hACE2 mice from a lethal challenge with SARS-CoV-2 beta variant, reducing mRNA and infectious viral loads in the lungs and in bronchoalveolar lavages, decreasing lung histopathological lesions and levels of proinflammatory cytokines in the lungs. Vaccination also elicited high titers of anti-S Th1-biased IgGs and neutralizing antibodies against ancestral SARS-CoV-2 Wuhan strain and VoCs alpha, beta, gamma, delta, and omicron. In addition, similar systemic and local SARS-CoV-2 S-specific CD4+ and CD8+ T-cell immune responses were elicited by both vaccine candidates after a single intranasal immunization in C57BL/6 mice. These preclinical data support clinical evaluation of MVA-S(3Pbeta) and MVA-S(3P), to explore whether they can diversify and potentially increase recognition and protection of SARS-CoV-2 VoCs.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.