{
  "abstract": "The COVID-19 pandemic highlighted the critical need for robust methods to monitor viral evolution and detect emerging variants of concern (VOCs). This study expanded an unsupervised clustering algorithm, based on Levenshtein distance, to track and predict variant predominance across six European countries from 2020 to January 2024. We also investigated the influence of genetic distances and containment strategies on hospitalization rates. Spike protein sequences were transformed into temporal chains. A deep neural network (DNN) was trained to classify emerging chains as likely dominant, while a CatBoost model assessed important variables, and simulations explored modifying vaccine genetic distance, containment measures, and vaccination coverage. Approximately 5,000 sequences per week enabled early chain detection within four weeks. The DNN achieved high classification performance for identifying future predominant chains within 3–4 weeks of detection. Genetic distance metrics between consecutive chains and between circulating and vaccine strains were among the most informative variables associated with hospitalization patterns. Model-based simulations suggested that scenarios involving improved vaccine matching or stronger containment measures were associated with lower predicted hospitalization burdens. Doubling vaccination coverage alone had minimal effect but showed additional reductions when combined with strict containment. Our findings from this integrated framework highlight the potential relevance of genetic distance metrics and public health interventions when assessing hospitalization risk associated with emerging variants. Author summary: In this study, we developed a new way to track how viruses like the one that causes COVID-19 change over time and predict which new versions might become widespread. During the pandemic, it became clear that we needed better tools to spot dangerous new variants early. By analyzing the genetic patterns of the virus’s spike proteins across six European countries from 2020 to early 2024, we created a computational model that can identify a rising variant within just three to four weeks of its first appearance. Beyond just tracking the virus, we investigated what actually drives hospitalizations. We found that the genetic distances between the current virus and previous versions and the distance with the vaccines correlated with how many people were hospitalized. Our simulations suggested that a significant impact on hospitalizations came from updated vaccines that closely matched the circulating virus and the public health measures. By providing a faster, more accurate way to monitor these genetic shifts, our framework offers a vital tool for health officials to make better-informed decisions and prepare for future outbreaks. From Sequences to Strategies: Early Detection of New SARS-CoV-2 Variants via Genetic Distance to Reduce Hospitalizations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Marika d'Avanzo"
    },
    {
      "name": "Aung Pone Myint"
    },
    {
      "name": "Giacomo Cacciapaglia"
    },
    {
      "name": "Stefan Hohenegger"
    },
    {
      "name": "Francesco Conventi"
    },
    {
      "name": "Marta Nunes"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2ep5tp406bn70.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014707",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014707",
  "id": "2ep5tp406bn70",
  "issns": [
    "1553-734X",
    "1553-7358"
  ],
  "landing_url": "https://hal.science/hal-05762838v1",
  "language": "en",
  "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": [
    [
      "hal",
      "0.phys"
    ],
    [
      "openalex",
      "2725|SARS-CoV-2 and COVID-19 Research"
    ],
    [
      "plos",
      "/Biology and life sciences/Evolutionary biology/Organismal evolution/Microbial evolution/Viral evolution"
    ],
    [
      "plos",
      "/Biology and life sciences/Immunology/Vaccination and immunization"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Medical microbiology/Microbial pathogens/Viral pathogens/Coronaviruses/SARS coronavirus/SARS CoV 2"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Microbial evolution/Viral evolution"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Virology/Viral evolution"
    ],
    [
      "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",
      "/Medicine and health sciences/Epidemiology/Disease surveillance"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Genetic epidemiology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Immunology/Vaccination and immunization"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Infectious diseases/Infectious disease control/Vaccines"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pathology and laboratory medicine/Pathogens/Microbial pathogens/Viral pathogens/Coronaviruses/SARS coronavirus/SARS CoV 2"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Preventive medicine/Vaccination and immunization"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Europe/European Union/Denmark"
    ]
  ],
  "openalex": "W7204896469",
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014707&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "PLOS",
  "ris": "https://research.draconitic.com/paper/2ep5tp406bn70.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:40Z",
      "native_id": "hal-05762838",
      "source": "hal",
      "url": "https://hal.science/hal-05762838v1"
    },
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pcbi.1014707",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014707"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:immunology",
      "subfield": "immunology"
    },
    {
      "field": "biology",
      "origin": "plos:microbiology",
      "subfield": "microbiology"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    },
    {
      "field": "physics",
      "origin": "hal:0.phys",
      "subfield": "general"
    }
  ],
  "title": "From sequences to strategies: Early detection of new SARS-CoV-2 variants via genetic distance to reduce hospitalizations",
  "title_text": "From sequences to strategies: Early detection of new SARS-CoV-2 variants via genetic distance to reduce hospitalizations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/2ep5tp406bn70",
  "venue": "PLoS Computational Biology",
  "versions": []
}