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Cell Rep Med . Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes

tetano

Editor, Senior Moderator
Cell Rep Med


. 2024 Jan 16;5(1):101361.
doi: 10.1016/j.xcrm.2023.101361. Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes

Ted Ling-Hu[SUP] 1 [/SUP], Lacy M Simons[SUP] 1 [/SUP], Taylor J Dean[SUP] 1 [/SUP], Estefany Rios-Guzman[SUP] 1 [/SUP], Matthew T Caputo[SUP] 2 [/SUP], Arghavan Alisoltani[SUP] 1 [/SUP], Chao Qi[SUP] 3 [/SUP], Michael Malczynski[SUP] 3 [/SUP], Timothy Blanke[SUP] 4 [/SUP], Lawrence J Jennings[SUP] 3 [/SUP], Michael G Ison[SUP] 5 [/SUP], Chad J Achenbach[SUP] 6 [/SUP], Paige M Larkin[SUP] 7 [/SUP], Karen L Kaul[SUP] 8 [/SUP], Ramon Lorenzo-Redondo[SUP] 1 [/SUP], Egon A Ozer[SUP] 1 [/SUP], Judd F Hultquist[SUP] 9 [/SUP]



Affiliations
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.

Keywords: COVID-19; SARS-CoV-2; confounders; genomic surveillance; molecular epidemiology; phylogenetics; severity modeling; variants of concern; viral evolution.

 
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