{
  "abstract": "Respiratory diseases cause considerable morbidity in autumn and winter and are a priority in public health monitoring. In Germany, they are subject to a number of surveillance systems, including both pathogen-specific and syndromic indicators. In this paper we present a collaborative multi-target and multi-model real-time forecasting system rolled out during the 2024/25 season, and discuss differences to earlier efforts carried out during the COVID-19 pandemic. We focus on general practitioner consultations for acute respiratory infections (ARI) and hospitalizations for severe acute respiratory infections (SARI), where a total of eight models were run to generate nowcasts and forecasts. In addition, we consider confirmed cases of seasonal influenza and respiratory syncytial virus (RSV) where one forecasting model each was available. As all indicators were subject to retrospective revisions, forecasting models were combined with a nowcasting step. For the SARI and ARI indicators, we combined the available models into an ensemble and conducted detailed quantitative evaluations. Nowcasts overall showed convincing performance, even though for some models Christmas break effects led to an upward bias in early January. Forecasts were overall well-calibrated and most models outperformed simple benchmark models. When taking into account age-stratified targets, most observed performance differences to a persistence baseline were statistically significant. Anticipating the peak timing and magnitude, however, proved to be challenging, with many models predicting too flat curves with a too early turnaround (e.g., in late January rather than mid-February for SARI). The combined ensemble forecast was among the best-performing approaches, but unlike in previous related projects did not consistently outperform individual models. We conclude by discussing learnings on the organization of collaborative forecasting projects in post-COVID-19 times.",
  "abstract_shown": true,
  "affiliations": [
    "Karlsruhe Institute of Technology"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 19,
  "authors": [
    {
      "name": "J. Bracher"
    },
    {
      "name": "D. Wolffram"
    },
    {
      "name": "R. Amaral Lind"
    },
    {
      "name": "N. Bardeck"
    },
    {
      "name": "M. Boehm"
    },
    {
      "name": "S. Contreras"
    },
    {
      "name": "P. Doenges"
    },
    {
      "name": "F. Guenther"
    },
    {
      "name": "R. Kaiser"
    },
    {
      "name": "J. van de Kassteele"
    },
    {
      "name": "A. Kuhlmann"
    },
    {
      "name": "B. Lange"
    },
    {
      "name": "B. Nemcova"
    },
    {
      "name": "V. Priesemann"
    },
    {
      "name": "U. Reinacher"
    },
    {
      "name": "I. Rodiah"
    },
    {
      "name": "F. Sandmann"
    },
    {
      "name": "the RESPINOW Study Group"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Schienle"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/djw1msd3q1nwf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.20.26358471",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.07.20.26358471v2.full",
  "id": "djw1msd3q1nwf",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.07.20.26358471v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "epidemiology"
    ],
    [
      "openalex",
      "2713|Data-Driven Disease Surveillance"
    ]
  ],
  "openalex": "W7169847208",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.07.20.26358471v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/djw1msd3q1nwf.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:21:04Z",
      "native_id": "10.64898/2026.07.20.26358471",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.07.20.26358471v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Multi-model forecasting of respiratory disease activity in Germany during the 2024-2025 season",
  "title_text": "Multi-model forecasting of respiratory disease activity in Germany during the 2024-2025 season",
  "type": "preprint",
  "updated_at": "2026-11-05T00:20:36Z",
  "url": "https://research.draconitic.com/paper/djw1msd3q1nwf",
  "venue": "medRxiv",
  "versions": []
}