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Effect of Vaccination on SARS-CoV-2 Spread

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2023-11-21

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Krasilnikova, Lydia. 2023. Effect of Vaccination on SARS-CoV-2 Spread. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.

Abstract

The July 2021 Provincetown, Massachusetts, Delta variant outbreak was the first large SARS-CoV-2 outbreak in vaccinated individuals; the Delta wave and the Omicron waves that followed it infected most individuals around the world. I examine the effect of vaccination on the spread of SARS-CoV-2 alongside the emergence of new variants in two contexts. First, I examine SARS-CoV-2 genomes collected from 467 individuals during the large Delta variant Provincetown outbreak. In collaboration with the Massachusetts Department of Public Health (MA DPH), we demonstrate through both contact tracing data and transmission inference from genomic data that vaccinated individuals were not only infected but also contributed to spread. We find that the Provincetown outbreak, however, had minimal contribution to the Delta wave that followed it: the outbreak was contained, likely due to vaccination and contact tracing efforts. Second, I examine SARS-CoV-2 genomes from over 120,000 SARS-CoV-2 samples collected from September 2021 through December 2022 comprising variants Delta, BA.1, BA.2, BA.2.12.1, BA.2.75, BA.4, BA.5, BQ.1, and XBB. Working again with the MA DPH, we show that while vaccinated individuals do get infected and do contribute to spread, vaccination is associated with decreased likelihood of both infection and onward transmission.

The COVID-19 pandemic has shown us the importance of not only fast detection of known pathogens, but also the discovery of novel pathogens, which is being made possible by advances in metagenomic sequencing. Increasingly large databases and increasingly large datasets, however, have made the current most accurate genomic data search algorithms unscalable, necessitating fast yet still accurate algorithms to best identify potential causal pathogens. I present a scalable blast-based pipeline for taxon identification with tenfold speed-up and no loss in sensitivity detecting known viruses compared to off-the-shelf megablast. I furthermore show how to augment this pipeline to detect novel viruses.

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Biology

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