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

doi:10.1016/j.xcrm.2023.101361

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

Medicine, Infectious Diseases Division

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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 language	English (US)
Article number	101361
Journal	Cell Reports Medicine
Volume	5
Issue number	1
DOIs	https://doi.org/10.1016/j.xcrm.2023.101361
State	Published - Jan 16 2024

Funding

The authors would like to thank Hannah H. Nam, Samuel Gatesy, Scott C. Roberts, William J. Cisneros, and Daphne Cornish for their assistance with specimen biobanking. This research was supported in part through the computational resources and staff contributions provided for the Quest high-performance computing facility at Northwestern University , which is jointly supported by the Office of the Provost , the Office for Research , and Northwestern University Information Technology . Clinical data collection was supported in part by the Northwestern Medicine Enterprise Data Warehouse . We gratefully acknowledge all data contributors, i.e., the authors and their originating laboratories responsible for obtaining the specimens and their submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based. Funding for this work was provided by a Dixon Translational Research grant made possible by the generous support of the Dixon Family Foundation (E.A.O. and J.F.H.), two COVID-19 Supplemental Research awards from the National Institutes of Health ( NIH ) National Center for Advancing Translational Sciences ( NCATS ; grant UL1 TR001422 to J.F.H. and grant UL1 TR002389 to J.F.H., E.A.O., and R.L.-R.), a supplement to the Northwestern University Cancer Center (grant P30 CA060553 to J.F.H.), the NIH -supported Third Coast CFAR (grant P30 AI117943 to R.L.-R. and J.F.H.), NIH grant R21 AI163912 (J.F.H.), NIH grant U19 AI135964 (E.A.O.), and a generous contribution from the Walder Foundation’s Chicago Coronavirus Assessment Network (Chicago CAN) Initiative (J.F.H., E.A.O., and R.L.-R.). The funding sources had no role in the study design, data collection, analysis, interpretation, or writing of the report.

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.xcrm.2023.101361

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Ling-Hu, T., Simons, L. M., Dean, T. J., Rios-Guzman, E., Caputo, M. T., Alisoltani, A., Qi, C., Malczynski, M., Blanke, T., Jennings, L. J., Ison, M. G., Achenbach, C. J., Larkin, P. M., Kaul, K. L., Lorenzo-Redondo, R., Ozer, E. A., & Hultquist, J. F. (2024). Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes. Cell Reports Medicine, 5(1), Article 101361. https://doi.org/10.1016/j.xcrm.2023.101361

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title = "Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes",

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",

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

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Ling-Hu, T, Simons, LM, Dean, TJ, Rios-Guzman, E, Caputo, MT, Alisoltani, A, Qi, C, Malczynski, M, Blanke, T, Jennings, LJ, Ison, MG, Achenbach, CJ, Larkin, PM, Kaul, KL, Lorenzo-Redondo, R , Ozer, EA & Hultquist, JF 2024, 'Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes', Cell Reports Medicine, vol. 5, no. 1, 101361. https://doi.org/10.1016/j.xcrm.2023.101361

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AU - Simons, Lacy M.

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AU - Rios-Guzman, Estefany

AU - Caputo, Matthew T.

AU - Alisoltani, Arghavan

AU - Qi, Chao

AU - Malczynski, Michael

AU - Blanke, Timothy

AU - Jennings, Lawrence J.

AU - Ison, Michael G.

AU - Achenbach, Chad J.

AU - Larkin, Paige M.

AU - Kaul, Karen L.

AU - Lorenzo-Redondo, Ramon

AU - Ozer, Egon A.

AU - Hultquist, Judd F.

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N2 - 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.

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

Abstract

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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