Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes

Ted Ling-Hu, Lacy M. Simons, Taylor J. Dean, Estefany Rios-Guzman, Matthew T. Caputo, Arghavan Alisoltani, Chao Qi, Michael Malczynski, Timothy Blanke, Lawrence J. Jennings, Michael G. Ison, Chad J. Achenbach, Paige M. Larkin, Karen L. Kaul, Ramon Lorenzo-Redondo, Egon A. Ozer, Judd F. Hultquist*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with enhanced transmissibility and immune escape have emerged periodically throughout the coronavirus disease 2019 (COVID-19) pandemic, but the impact of these variants on disease severity has remained unclear. In this single-center, retrospective cohort study, we examined the association between SARS-CoV-2 clade and patient outcome over a two-year period in Chicago, Illinois. Between March 2020 and March 2022, 14,252 residual diagnostic specimens were collected from SARS-CoV-2-positive inpatients and outpatients alongside linked clinical and demographic metadata, of which 2,114 were processed for viral whole-genome sequencing. When controlling for patient demographics and vaccination status, several viral clades were associated with risk for hospitalization, but this association was negated by the inclusion of population-level confounders, including case count, sampling bias, and shifting standards of care. These data highlight the importance of integrating non-virological factors into disease severity and outcome models for the accurate assessment of patient risk.

Original languageEnglish (US)
Article number101361
JournalCell Reports Medicine
Volume5
Issue number1
DOIs
StatePublished - Jan 16 2024

Keywords

  • COVID-19
  • SARS-CoV-2
  • confounders
  • genomic surveillance
  • molecular epidemiology
  • phylogenetics
  • severity modeling
  • variants of concern
  • viral evolution

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology

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