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Abstract Background The Covid-19 pandemic has been characterized by the emergence of novel SARS-CoV-2 variants, each with distinct properties influencing transmission dynamics, immune escape, and virulence, which, in turn, influence their impact on local populations. Swift analysis of the properties of newly emerged variants is essential in the initial days and weeks to enhance readiness and facilitate the scaling of clinical and public health system responses. Methods This paper introduces a two-variant metapopulation compartmental model of disease transmission to simulate the dynamics of disease transmission during a period of transition to a newly dominant strain. Leveraging novel S-gene dropout analysis data and genomic sequencing data, combined with confirmed Covid-19 case data, we estimate the epidemiological characteristics of the Omicron variant, which replaced the Delta variant in late 2021 in Philadelphia, PA. We utilized a grid-search method to identify plausible combinations of model parameters, followed by an ensemble adjustment Kalman filter for parameter inference. Results The model successfully estimated key epidemiological parameters; we estimated the ascertainment rate of 0.22 (95% credible interval 0.15–0.29) and transmission rate of 5.0 (95% CI 2.4–6.6) for the Omicron variant. Conclusions The study demonstrates the potential for this model-inference framework to provide real-time insights during the emergence of novel variants, aiding in timely public health responses.

ABSTRACT The severe acute respiratory coronavirus-2 (SARS-CoV-2) is the cause of the global outbreak of COVID-19. Evidence suggests that the virus is evolving to allow efficient spread through the human population, including vaccinated individuals. Here, we report a study of viral variants from surveillance of the Delaware Valley, including the city of Philadelphia, and variants infecting vaccinated subjects. We sequenced and analyzed complete viral genomes from 2621 surveillance samples from March 2020 to September 2021 and compared them to genome sequences from 159 vaccine breakthroughs. In the early spring of 2020, all detected variants were of the B.1 and closely related lineages. A mixture of lineages followed, notably including B.1.243 followed by B.1.1.7 (alpha), with other lineages present at lower levels. Later isolations were dominated by B.1.617.2 (delta) and other delta lineages; delta was the exclusive variant present by the last time sampled. To investigate whether any variants appeared preferentially in vaccine breakthroughs, we devised a model based on Bayesian autoregressive moving average logistic multinomial regression to allow rigorous comparison. This revealed that B.1.617.2 (delta) showed 3-fold enrichment in vaccine breakthrough cases (odds ratio of 3; 95% credible interval 0.89-11). Viral point substitutions could also be associated with vaccine breakthroughs, notably the N501Y substitution found in the alpha, beta and gamma variants (odds ratio 2.04; 95% credible interval of1.25–3.18). This study thus overviews viral evolution and vaccine breakthroughs in the Delaware Valley and introduces a rigorous statistical approach to interrogating enrichment of breakthrough variants against a changing background. IMPORTANCE SARS-CoV-2 vaccination is highly effective at reducing viral infection, hospitalization and death. However, vaccine breakthrough infections have been widely observed, raising the question of whether particular viral variants or viral mutations are associated with breakthrough. Here, we report analysis of 2621 surveillance isolates from people diagnosed with COVID-19 in the Delaware Valley in southeastern Pennsylvania, allowing rigorous comparison to 159 vaccine breakthrough case specimens. Our best estimate is a 3-fold enrichment for some lineages of delta among breakthroughs, and enrichment of a notable spike substitution, N501Y. We introduce statistical methods that should be widely useful for evaluating vaccine breakthroughs and other viral phenotypes.

While infant cues are often assumed to innately motivate maternal response, recent research highlights how the neural coding of infant cues is altered through maternal care. Infant vocalizations are important social signals for caregivers, and evidence from mice suggests that experience caring for mouse pups induces inhibitory plasticity in the auditory cortex (AC), though the molecular mediators for such AC plasticity during the initial pup experience are not well delineated. Here, we used the maternal mouse communication model to explore whether transcription in AC of a specific, inhibition-linked, memory-associated gene, brain-derived neurotrophic factor (Bdnf) changes due to the very first pup caring experience hearing vocalizations, while controlling for the systemic influence of the hormone estrogen. Ovariectomized and estradiol or blank-implanted virgin female mice hearing pup calls with pups present had significantly higher AC exon IV Bdnf mRNA compared to females without pups present, suggesting that the social context of vocalizations induces immediate molecular changes at the site of auditory cortical processing. E2 influenced the rate of maternal behavior but did not significantly affect Bdnf mRNA transcription in the AC. To our knowledge, this is the first time Bdnf has been associated with processing social vocalizations in the AC, and our results suggest that it is a potential molecular component responsible for enhancing future recognition of infant cues by contributing to AC plasticity.