SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and natural immunity (preprint)

Background: Throughout the COVID-19 pandemic, the SARS-CoV-2 virus has continued to evolve, with new variants outcompeting existing variants and often leading to different dynamics of disease spread. Methods: In this paper, we performed a retrospective analysis using longitudinal sequencing data to characterize differences in the speed, calendar timing, and magnitude of 13 SARS-CoV-2 variant waves/transitions for 215 countries and sub-country regions, between October 2020 and October 2022. We then clustered geographic locations in terms of their variant behavior across all Omicron variants, allowing us to identify groups of locations exhibiting similar variant transitions. Finally, we explored relationships between heterogeneity in these variant waves and time-varying factors, including vaccination status of the population, governmental policy, and the number of variants in simultaneous competition. Findings: This work demonstrates associations between the behavior of an emerging variant and the number of co-circulating variants as well as the demographic context of the population. We also observed an association between high vaccination rates and variant transition dynamics prior to the Mu and Delta variant transitions. Interpretation: These results suggest the behavior of an emergent variant may be sensitive to the immunologic and demographic context of its location. Additionally, this work represents the most comprehensive characterization of variant transitions globally to date. Funding: Laboratory Directed Research and Development (LDRD), Los Alamos National Laboratory

Recombination and low-diversity confound homoplasy-based methods to detect the effect of SARS-CoV-2 mutations on viral transmissibility (preprint)

The SARS-CoV-2 variant carrying the Spike protein mutation G614 was first detected in late January 2020 and within a few months became the dominant form globally. In the months that followed, many studies, both in vitro and in animal models, showed that variants carrying this mutation were more infectious and more readily transmitted than the ancestral Wuhan form. Here we investigate why a recently published study by van Dorp et al. failed to detect such higher transmissibility of the G614 variant using homoplasy-based methods. We show that both low diversity and recombination confound the methods utilized by van Dorp et al. and significantly decrease their sensitivity. Furthermore, though they claim no evidence of recombination in their dataset, we and several other studies identify a subset of the sequences as recombinants, possibly enough to affect their statistic adversely.

Information retrieval in an infodemic: the case of COVID-19 publications (preprint)

In the context of searching for COVID-19 related scientific literature, we present an information retrieval methodology for effectively finding relevant publications for different information needs. We discuss different components of our architecture consisting of traditional information retrieval models, as well as modern neural natural language processing algorithms. We present recipes to better adapt these components to the case of an infodemic, where, from one hand, the number of publications has an exponential growth and, from the other hand, the topics of interest evolve as the pandemic progresses. The methodology was evaluated in the TREC-COVID challenge, achieving competitive results with top ranking teams participating in the competition. In retrospect to this challenge, we provide additional insights with further useful impacts.