{
  "abstract": "Objective: Using Fujian’s multi-source Coronavirus Disease 2019 (COVID-19) surveillance system, this study compared epidemiological features and indicator associations of two successive epidemic waves to optimize epidemic surveillance and prevention strategies.\n\nMethods: Multi-source surveillance data were collected from week 49 of 2022 (the week of December 7, 2022, when the 10 optimization measures were issued) to week 29 of 2023 (July 17–23, 2023) in Fujian Province, including reported COVID-19 cases, fever clinic visits, population nucleic acid testing, sentinel hospital surveillance, hospitalized severe cases, and viral variants. Descriptive epidemiology, Spearman correlation, and lagged cross-correlation analyses were adopted for data evaluation.\n\nResults: The first wave showed a sharp peak pattern, with most indicators peaking around weeks 51–52/2022 and reported cases peaking at week 1/2023; the second wave exhibited a gradual peak at week 21/2023. Peak levels of the second wave were 10.7–99.9% lower across indicators, although the markedly reduced nucleic acid testing volume and changed surveillance conditions warrant cautious interpretation of cross-wave comparisons. The interval between the two epidemic peaks was about 5 months, and severe cases consistently lagged infection peaks by approximately 2 weeks. Indicators were strongly correlated, with the highest correlation between sentinel and nucleic acid positivity rates (rs = 0.92, p < 0.001); lagged cross-correlation analysis indicated that these two indicators moved essentially in synchrony. The XBB variant took approximately 4 weeks from initial local detection to predominance alongside the second epidemic peak.\n\nConclusion: The two observed epidemic peaks were approximately 5 months apart, and multi-source surveillance indicators showed high temporal concordance. However, lagged cross-correlation indicated that the indicators moved essentially in synchrony, so sentinel surveillance should be regarded as a complementary monitoring tool rather than a proven early-warning measure. The XBB variant reached predominance approximately 4 weeks after initial local detection in this episode, a descriptive observation that cannot be generalized as a validated predictive tool. These findings facilitate multi-wave epidemic preparedness and surveillance improvement.",
  "abstract_shown": true,
  "affiliations": [
    "Fujian Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research",
    "School of Public Health, Fujian Medical University",
    "Xiamen Center for Disease Control and Prevention",
    "Chinese Field Epidemiology Training Program, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jiawei Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "Senshuang Zheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhifang Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jingjing Cai"
    },
    {
      "affs": [
        0
      ],
      "name": "Cailin Chen"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Yuedong Lin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jianming Ou"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Hansong Zhu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Wenjing Ye"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Shenggen Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/32e9c9qjz2cra.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1857657",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1857657/full",
  "id": "32e9c9qjz2cra",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1857657/full",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RA1-1270"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/32e9c9qjz2cra.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:24Z",
      "native_id": "10.3389/fpubh.2026.1857657",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1857657/full"
    },
    {
      "first_seen": "2026-10-31T14:22:32Z",
      "native_id": "0a222251c43743c4b17a147d73b5df93",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fpubh.2026.1857657"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RA1-1270",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Multi-source COVID-19 surveillance in Fujian, China: epidemiological features of two epidemic waves and public health implications",
  "title_text": "Multi-source COVID-19 surveillance in Fujian, China: epidemiological features of two epidemic waves and public health implications",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:22:32Z",
  "url": "https://research.draconitic.com/paper/32e9c9qjz2cra",
  "venue": "Frontiers in Public Health",
  "versions": []
}