Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Oct;45(10):3030-3044.
doi: 10.1111/risa.70072. Epub 2025 Jul 6.

Personalized risk score for post-COVID-19 condition: Bayesian directed acyclic graphic approach

Sam Li-Sheng Chen  1 , Chen-Yang Hsu  2   3 , Tin-Yu Lin  4 , Amy Ming-Fang Yen  1 , Tony Hsiu-Hsi Chen  4
Affiliations

Personalized risk score for post-COVID-19 condition: Bayesian directed acyclic graphic approach

Sam Li-Sheng Chen et al. Risk Anal. 2025 Oct.

Abstract

Post-COVID-19 condition (PCC) has gained traction currently in the post-pandemic era. To address this, we utilized a Bayesian directed acyclic graphic (DAG) model to develop a personalized composite risk score (CRS) for PCC, based on the tabular data derived from a comprehensive meta-analysis. Our risk assessment model incorporates 215 combinations of risk factors, including personal demographic and health-related profiles, across 41 studies involving over 860,000 COVID-19 cases. The CRS ranges from 0 to 500, categorizing patients into risk quartiles and estimating PCC probability across SARS-CoV-2 variants of concerns, including Wild/D614G/Alpha, Delta, and Omicron BA.1/BA.2. External validation demonstrated accurate predictions, though higher risk scores showed slight deviations, particularly in BA.5 Omicron subset. The risk assessment model is not only adaptable for incorporating new evidence as SARS-CoV-2 subvariants emerge but also very valuable in facilitating the optimal individualized medical care for PCC patients and prioritizing a spectrum of risk groups for early PCC diagnosis. Notably, the adaptability of Bayesian DAG model enhances PCC risk prediction, enabling data integration for evolving SARS-CoV-2 contexts and informing healthcare resource allocation for high-risk groups.

Keywords: Bayesian; post‐COVID‐19 condition; risk assessment.

PubMed Disclaimer

Figures

FIGURE 1
FIGURE 1
Bayesian directed acyclic graphic (DAG) model for estimating and predicting individual risk of post‐COVID‐19 condition (PCC).
FIGURE 2
FIGURE 2
Composite risk score distribution and the posterior probability of post‐COVID‐19 condition (PCC).
FIGURE 3
FIGURE 3
Observed, estimated, and predictive probability of post‐COVID‐19 condition (PCC) by risk score using Israel data.
FIGURE 4
FIGURE 4
Observed, estimated, and predictive probability of post‐COVID‐19 condition (PCC) by risk score using Japan data.
FIGURE 5
FIGURE 5
Observed, estimated, and predictive probability of post‐COVID‐19 condition (PCC) by risk score with prior information using Japan data.
FIGURE C1
FIGURE C1
Observed, estimated, and predictive probability of post‐COVID‐19 condition (PCC) with risk scores by fixed‐effect model in Israel data.
FIGURE C2
FIGURE C2
Observed, estimated, and predictive probability of post‐COVID‐19 condition (PCC) with risk scores by fixed‐effect model in Japan data.
See this image and copyright information in PMC

References

    1. Antonelli, M. , Pujol, J. C. , Spector, T. D. , Ourselin, S. , & Steves, C. J. (2022). Risk of long COVID associated with delta versus omicron variants of SARS‐CoV‐2. Lancet (London, England), 399(10343), 2263–2264. - PMC - PubMed
    1. Arjun, M. C. , Singh, A. K. , Pal, D. , Das, K. , G, A., Venkateshan, M. , Mishra, B. , Patro, B. K. , Mohapatra, P. R. , & Subba, S. H. (2022). Characteristics and predictors of long COVID among diagnosed cases of COVID‐19. PLoS ONE, 17(12), Article e0278825. - PMC - PubMed
    1. Berkey, C. S. , Hoaglin, D. C. , Mosteller, F. , & Colditz, G. A. (1995). A random‐effects regression model for meta‐analysis. Statistics in Medicine, 14(4), 395–411. - PubMed
    1. Galán, M. , Vigón, L. , Fuertes, D. , Murciano‐Antón, M. A. , Casado‐Fernández, G. , Domínguez‐Mateos, S. , Mateos, E. , Ramos‐Martín, F. , Planelles, V. , Torres, M. , Rodríguez‐Mora, S. , López‐Huertas, M. R. , & Coiras, M. (2022). Persistent overactive cytotoxic immune response in a Spanish cohort of individuals with long‐COVID: Identification of diagnostic biomarkers. Frontiers in Immunology, 13, Article 848886. - PMC - PubMed
    1. Glass, G. V. (1976). Primary, secondary, and meta‐analysis of research. Educational Researcher, 5(10), 3–8.

LinkOut - more resources