{
  "abstract": "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 clinical and immune features, and ultimately identified spike-specific nasal secretory immunoglobulin A (sIgA) and spike-specific CD4⁺ central memory T cells (TCM) as key predictors. Developed using the extreme gradient boosting (XGB) algorithm, the prediction model achieved an AUC of 0.835 in the internal validation cohort and demonstrated strong discrimination in the external validation cohort. SHAP analysis revealed that higher nasal spike-specific sIgA was associated with lower risk, while spike-specific CD4⁺ TCM cells exhibited a non-linear association with reinfection. This interpretable immune-based model provides a framework for immune-informed risk stratification, enabling better prediction and management of SARS-CoV-2 reinfection risk in settings with heterogeneous population immunity.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China.",
    "Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Joint Institute of Nanjing Drum Tower Hospital for Life and Health, College of Life Science, Nanjing Normal University, Nanjing, People's Republic of China.",
    "Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, People's Republic of China.",
    "Center of Medical Big Data, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China.",
    "School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, People's Republic of China.",
    "Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Clinical College of Jiangsu University, Nanjing, People's Republic of China.",
    "Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, People's Republic of China.",
    "Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jinqiu Ran"
    },
    {
      "affs": [
        1
      ],
      "name": "Wen Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Wanying Zhang"
    },
    {
      "affs": [
        3
      ],
      "name": "Hui Huang"
    },
    {
      "affs": [
        4
      ],
      "name": "Lejun Gong"
    },
    {
      "affs": [
        0
      ],
      "name": "Shengxia Yin"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu Chen"
    },
    {
      "affs": [
        5
      ],
      "name": "Aofan Wang"
    },
    {
      "affs": [
        4
      ],
      "name": "Huijin Min"
    },
    {
      "affs": [
        6
      ],
      "name": "Liguo Zhu",
      "orcid": "0000-0002-0636-4089"
    },
    {
      "affs": [
        6
      ],
      "name": "Jinjin Chu"
    },
    {
      "affs": [
        6
      ],
      "name": "Yuanfang Qin"
    },
    {
      "affs": [
        0
      ],
      "name": "Rui Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Chao Wu"
    },
    {
      "affs": [
        7
      ],
      "name": "Yuxin Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/36eh19s1t92vf.bib",
  "cited_by": 0,
  "doi": "10.1080/22221751.2026.2746253",
  "fulltext_url": "https://europepmc.org/articles/PMC13700530",
  "id": "36eh19s1t92vf",
  "issns": [
    "2222-1751"
  ],
  "landing_url": "https://europepmc.org/article/MED/42908321",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2725|SARS-CoV-2 and COVID-19 Research"
    ]
  ],
  "openalex": "W7223182985",
  "pdf_url": "https://europepmc.org/articles/PMC13700530?pdf=render",
  "pmcid": "PMC13700530",
  "pmid": "42908321",
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/36eh19s1t92vf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42908321",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42908321"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2725|SARS-CoV-2 and COVID-19 Research",
      "subfield": "infectious-disease"
    }
  ],
  "title": "An interpretable machine learning model integrating mucosal and cellular immunity to predict SARS-CoV-2 reinfection: a multicenter cohort study.",
  "title_text": "An interpretable machine learning model integrating mucosal and cellular immunity to predict SARS-CoV-2 reinfection: a multicenter cohort study.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/36eh19s1t92vf",
  "venue": "Emerging microbes & infections",
  "versions": []
}