aceAn interpretable machine learning model integrating mucosal and cellular immunity to predict SARS-CoV-2 reinfection: a multicenter cohort study
SARS-CoV-2 reinfection remains a significant public health challenge that requires accurate risk assessment across populations. Using prospectively collected data from two independent cohorts, we developed and externally validated an interpretable machine learning model for predicting symptomatic SARS-CoV-2 reinfection. The cohort comprised 1,156 adults from Wuxi and Nantong, China, with a median follow-up duration of 10 months. Reinfection occurred in 128 participants (11.1%). The model integrated eighteen clin…
An XGBoost model using nasal spike-specific sIgA and CD4+ central memory T cell measures, among other features, can stratify adults by symptomatic SARS-CoV-2 reinfection risk, with external validation.
The abstract gives no numeric external validation performance, and the data come from two cohorts in China, so generalizability to other populations and settings is not established.
Evidence
- Peer-reviewedEmerging Microbes & Infections2026-10-21
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Truvace Impact Record TRV-2026-2218, v1: “An interpretable machine learning model integrating mucosal and cellular immunity to predict SARS-CoV-2 reinfection: a multicenter cohort study.” Truvace, 2026-10-23. /record/TRV-2026-2218 (accessed at citation time). sha256 4ab4ee6b52e554af…
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