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Journal article
Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning
Computers in biology and medicine, v 149, 105969
Oct 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports. Fortuitously, a subset of GISAID, the global SARS-CoV-2 sequence repository, includes “patient status” metadata that can indicate whether a sequence record is associated with mild or severe disease. While GISAID lacks data on comorbidities relevant to severity, such as obesity and chronic disease, it does include metadata for age and sex to use as additional attributes in modeling. With these caveats, previous efforts have demonstrated that genotype-patient status models can be fit to GISAID data, particularly when country-of-origin is used as an additional feature. But are these models robust and biologically meaningful? This paper shows that, in fact, temporal and geographic biases in sequences submitted to GISAID, as well as the evolving pandemic response, particularly reduction in severe disease due to vaccination, create complex issues for model development and interpretation. This paper poses a potential solution: efficient mixed effects machine learning using GPBoost, treating country as a random effect group. Training and validation using temporally split GISAID data and emerging Omicron variants demonstrates that GPBoost models are more predictive of the impact of spike protein mutations on patient outcomes than fixed effect XGBoost, LightGBM, random forests, and elastic net logistic regression models.
•We propose to use global SARS-CoV-2 sequence data to predict severe COVID-19 disease.•Global sequence and patient outcome data vary greatly over time and between regions.•Mixed effects machine learning with GPBoost outperform fixed effect methods.•Trained models can provide early warnings for risks of emerging viral variants.•GPBoost modeling can also identify key mutations of potential clinical interest.
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Details
- Title
- Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning
- Creators
- Bahrad A. Sokhansanj - Drexel UniversityGail L. Rosen
- Publication Details
- Computers in biology and medicine, v 149, 105969
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000877199500001
- Scopus ID
- 2-s2.0-85136539879
- Other Identifier
- 991019173852404721
UN Sustainable Development Goals (SDGs)
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- Web of Science research areas
- Biology
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Mathematical & Computational Biology