Machine learning-supported framework for the classification of mpox infection and MVA immunization from multiplexed serology data
The 2022 global mpox outbreak highlighted the risk of zoonotic diseases establishing sustained transmission in human populations and underscored the need for accurate serological tools to monitor orthopoxvirus exposure. However, cross-reactive antibodies induced by Modified Vaccinia Ankara (MVA) vaccination make it difficult to discriminate between monkeypox virus (MPXV) infection and vaccination-induced immunity. Here we present a machine learning (ML)-assisted bead-based serological multiplex assay that distinguishes MPXV infection from MVA vaccination and pre-immune sera by targeting antibody responses to 15 poxviral antigens. Of the six algorithms tested, the Gradient Boosting Classifier (GBC) achieves the highest performance (F1 = 0.83) in sera from the 2022 outbreak and from a follow-up epidemiological cohort of at-risk men who have sex with men (MSM; n = 1,260). In an independent validation cohort (n = 143), GBC (F1 = 0.70) robustly detects MPXV infections, including breakthrough cases, with 88% specificity and 92% sensitivity. Integrating ML with high-dimensional serology enables accurate cross-sectional classification of orthopoxvirus immune status and provides a scalable framework for mpox serosurveillance and outbreak preparedness.
Authors
- Nicole Friedrich (ORCID: https://orcid.org/0000-0003-4373-9713)
- Katharina Ladewig (ORCID: https://orcid.org/0000-0003-3397-1308)
- Marica Grossegesse (ORCID: https://orcid.org/0000-0002-9369-8203)
- Fatimanur Bayram (ORCID: https://orcid.org/0000-0002-9249-5850)
- Andreas Nitsche (ORCID: https://orcid.org/0000-0001-8185-3176)
- Klaus Jansen (ORCID: https://orcid.org/0000-0002-4878-9421)
- Fridolin Treindl
- Ulrich Marcus (ORCID: https://orcid.org/0000-0002-3143-2717)
- Brigitte G. Dorner (ORCID: https://orcid.org/0000-0002-5100-2546)
- Annette Mankertz (ORCID: https://orcid.org/0000-0002-0131-5739)
- Mai P. Nguyen (ORCID: https://orcid.org/0000-0003-4138-2677)
- Uwe Koppe (ORCID: https://orcid.org/0000-0001-9718-5811)
- Daniel Stern (ORCID: https://orcid.org/0000-0001-9057-4283)
- Shakhnaz Akhmedova (ORCID: https://orcid.org/0000-0003-2927-1974)
- Denis Beslic (ORCID: https://orcid.org/0000-0001-6346-7896)
- Hans Werner Mages
- Janine Michel (ORCID: https://orcid.org/0000-0001-6227-3125)
- Rebecca Surtees (ORCID: https://orcid.org/0000-0003-0265-8407)
- Akin Sesver (ORCID: https://orcid.org/0000-0003-2954-2957)
- Martin Skiba
- Thomas Rinner
- Nils Körber (ORCID: https://orcid.org/0009-0003-2506-4297)
Institutions
- Robert Koch Institute (DE)
- Medizinische Universität Lausitz - Carl Thiem (DE)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-12-05
- DOI
- https://doi.org/10.1038/s41467-025-66994-0
- Primary Topic
- Poxvirus research and outbreaks
- Type
- article
- Field-Weighted Citation Impact
- 0.00