tetano
Editor, Senior Moderator
BMC Microbiol
. 2026 Jul 6.
doi: 10.1186/s12866-026-05360-6. Online ahead of print.
A population-based retrospective machine learning study of COVID-19 severity using integrated clinical and viral genomic data in Jiangsu Province, China
Xueyin Mei[SUP] #[/SUP][SUP] 1 [/SUP], Sidu Feng[SUP] #[/SUP][SUP] 1 [/SUP], Wanrong Xie[SUP] 1 [/SUP], Yi Sun[SUP] 1 [/SUP], Liqing Wang[SUP] 1 [/SUP], Xue Lin[SUP] 2 [/SUP], Huiyan Yu[SUP] 3 [/SUP], Jian Li[SUP] 4 [/SUP], Liguo Zhu[SUP] 5 [/SUP]
Affiliations
Background: As coronavirus disease 2019 (COVID-19) has transitioned into an endemic phase characterized by sustained transmission and widespread hybrid immunity, understanding region-specific determinants of severe disease remains important for real-world risk stratification and public health planning.
Methods: A retrospective surveillance study was conducted using 5,072 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) whole-genome sequences linked to clinical metadata from 13 cities in Jiangsu Province (from January 2023 to December 2024). Features derived from clinical, viral genomic, and regional epidemiological domains were evaluated using five machine learning models and assessed on an independent 2024 cohort. Model interpretability was examined using SHapley Additive exPlanations (SHAP) analysis. Key mutations were further examined through epitope prediction, peptide-HLA docking and binding affinity assessments to explore potential immunological implications.
Results: Integrated multidimensional features demonstrated superior predictive performance compared with single-domain inputs. In the independent 2024 validation cohort, LightGBM achieved the best overall performance (F1-score = 0.603; AUC = 0.735). SHAP analysis identified age as the dominant model predictor, followed by the age-viral load interaction, regional location, vaccination status, and selected viral genomic features. Epitope prediction and structural analyses suggested L452W-associated changes in predicted peptide-HLA interaction patterns within the evaluated set of high-frequency HLA class I alleles in the Jiangsu population, providing candidate hypotheses for future experimental validation.
Conclusions: COVID-19 severity during the endemic phase appeared to reflect interactions among host susceptibility, viral genetic variation, and regional epidemiological context, with age and vaccination emerging as key predictive factors. This population-based, interpretable framework highlights clinically relevant risk-associated features and may support real-world risk stratification in ongoing and future infectious disease surveillance.
Keywords: COVID-19; Epitope; HLA class I; Machine learning; Risk stratification; SARS-CoV-2.
. 2026 Jul 6.
doi: 10.1186/s12866-026-05360-6. Online ahead of print.
A population-based retrospective machine learning study of COVID-19 severity using integrated clinical and viral genomic data in Jiangsu Province, China
Xueyin Mei[SUP] #[/SUP][SUP] 1 [/SUP], Sidu Feng[SUP] #[/SUP][SUP] 1 [/SUP], Wanrong Xie[SUP] 1 [/SUP], Yi Sun[SUP] 1 [/SUP], Liqing Wang[SUP] 1 [/SUP], Xue Lin[SUP] 2 [/SUP], Huiyan Yu[SUP] 3 [/SUP], Jian Li[SUP] 4 [/SUP], Liguo Zhu[SUP] 5 [/SUP]
Affiliations
- PMID: 42410338
- DOI: 10.1186/s12866-026-05360-6
Background: As coronavirus disease 2019 (COVID-19) has transitioned into an endemic phase characterized by sustained transmission and widespread hybrid immunity, understanding region-specific determinants of severe disease remains important for real-world risk stratification and public health planning.
Methods: A retrospective surveillance study was conducted using 5,072 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) whole-genome sequences linked to clinical metadata from 13 cities in Jiangsu Province (from January 2023 to December 2024). Features derived from clinical, viral genomic, and regional epidemiological domains were evaluated using five machine learning models and assessed on an independent 2024 cohort. Model interpretability was examined using SHapley Additive exPlanations (SHAP) analysis. Key mutations were further examined through epitope prediction, peptide-HLA docking and binding affinity assessments to explore potential immunological implications.
Results: Integrated multidimensional features demonstrated superior predictive performance compared with single-domain inputs. In the independent 2024 validation cohort, LightGBM achieved the best overall performance (F1-score = 0.603; AUC = 0.735). SHAP analysis identified age as the dominant model predictor, followed by the age-viral load interaction, regional location, vaccination status, and selected viral genomic features. Epitope prediction and structural analyses suggested L452W-associated changes in predicted peptide-HLA interaction patterns within the evaluated set of high-frequency HLA class I alleles in the Jiangsu population, providing candidate hypotheses for future experimental validation.
Conclusions: COVID-19 severity during the endemic phase appeared to reflect interactions among host susceptibility, viral genetic variation, and regional epidemiological context, with age and vaccination emerging as key predictive factors. This population-based, interpretable framework highlights clinically relevant risk-associated features and may support real-world risk stratification in ongoing and future infectious disease surveillance.
Keywords: COVID-19; Epitope; HLA class I; Machine learning; Risk stratification; SARS-CoV-2.