{
  "abstract": "Leading indicator analyses, a type of time series analysis, are frequently used to assess how epidemic surveillance signals relate to one another. Strong leading indicators can drive early, and better decisions in public health systems. We conducted a narrative review of leading indicator studies and evaluated methodological limitations in their reporting and analysis, which we used to generate recommendations in retrospective surveillance analyses. Achievable recommendations are provided based on the reporting and analysis components of real-time epidemic time series analysis. We provide contextual examples from the literature of previous studies demonstrating good practice and highlighting areas for improvement. We present a checklist and workflow that emphases real-time constraints. These recommendations include causal mechanism reporting, sample coverage and bias, reporting delays and data revisions, multiple event measurement, spatio-temporal granularly, time varying relationships, smoothing, transformations, uncertainty, and predictive utility. By following these good practices, analysts can improve research on leading indicators to make analyses more directly actionable for public health decision makers. This work builds on the existing methodology literature by giving a broader framework for overall good practices.",
  "abstract_shown": true,
  "affiliations": [
    "Chief Data Officer Group, UK Health Security Agency, London, United Kingdom"
  ],
  "article_type": "Review",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jonathon Mellor"
    },
    {
      "name": "Maria Tang"
    },
    {
      "name": "Robert S. Paton"
    },
    {
      "name": "Thomas Ward"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/adzf28bme1m7r.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pdig.0001759",
  "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001759",
  "id": "adzf28bme1m7r",
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2713|Data-Driven Disease Surveillance"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Medical microbiology/Microbial pathogens/Viral pathogens/Coronaviruses/SARS coronavirus/SARS CoV 2"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Decision making"
    ],
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Decision making"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Viruses/RNA viruses/Coronaviruses/SARS coronavirus/SARS CoV 2"
    ],
    [
      "plos",
      "/Biology and life sciences/Organisms/Viruses/Viral pathogens/Coronaviruses/SARS coronavirus/SARS CoV 2"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Decision making"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Virus testing"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Disease surveillance/Infectious disease surveillance"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Epidemiological methods and statistics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Health care facilities/Hospitals"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Infectious diseases/Infectious disease control/Infectious disease surveillance"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pathology and laboratory medicine/Pathogens/Microbial pathogens/Viral pathogens/Coronaviruses/SARS coronavirus/SARS CoV 2"
    ],
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      "plos",
      "/People and places/Population groupings/Professions/Analysts"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Decision making"
    ]
  ],
  "openalex": "W7220437736",
  "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001759&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/adzf28bme1m7r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pdig.0001759",
      "source": "plos",
      "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001759"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Recommendations for the analysis of leading indicators in epidemic surveillance",
  "title_text": "Recommendations for the analysis of leading indicators in epidemic surveillance",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/adzf28bme1m7r",
  "venue": "PLOS Digital Health",
  "versions": []
}