{
  "abstract": "Multiple epidemic waves are often taken as evidence that simple compartmental models are structurally inadequate. Yet the key question is not whether a simple model can reproduce multiple peaks, but how much time-varying structure is minimally required to preserve observed epidemic-wave patterns and what can legitimately be inferred from such a representation. Using the susceptible-infectious-removed (SIR) model, we develop a framework for peak-preserving dynamic parsimony linking dynamical systems, inverse problems, statistical inference, and infectious-disease modeling. We establish exact multi-wave representability under time-varying transmission, characterize conditions for epidemic peaks and resurgence, and show the incidence-only non-identifiability of time-varying transmission and removal rates. Our main methodological contribution is minimal peak-preserving complexity (MPPC): the smallest dimension of a positive time-varying transmission function whose posterior-predictive trajectories preserve salient features, including peak count, timing, height, trough depth, and whole-curve agreement. We estimate MPPC using peak-aware approximate Bayesian computation and evaluate it through six reproducible stress tests, a Philippine COVID-19 reported-case proof of concept, and short-term future-knot projection. Across diverse data-generating mechanisms, low-dimensional time-varying SIR models can preserve aggregate epidemic-wave topology with modest dynamic complexity. However, accurate trajectory compression does not imply recovery of the underlying mechanism or reliable long-term counterfactual prediction. The framework therefore separates four often-conflated questions: representability, minimal dynamic complexity, mechanistic identifiability, and projection validity, providing a basis for parsimonious modeling of complex epidemic trajectories.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.05556",
  "author_total": 1,
  "authors": [
    {
      "name": "Jomar Fajardo Rabajante"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/901yw6y087hbh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.05556",
  "fulltext_url": null,
  "id": "901yw6y087hbh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.05556",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.PE"
    ],
    [
      "openalex",
      "2739|Mathematical and Theoretical Epidemiology and Ecology Models"
    ]
  ],
  "openalex": "W7220725338",
  "pdf_url": "https://arxiv.org/pdf/2610.05556",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/901yw6y087hbh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:11Z",
      "native_id": "2610.05556",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.05556"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.PE",
      "subfield": "evolutionary-biology"
    }
  ],
  "title": "How Simple Models Can Reproduce Multiple Epidemic Waves: Peak Topology, Minimal Dynamic Complexity, and Inference for Time-Varying Transmission",
  "title_text": "How Simple Models Can Reproduce Multiple Epidemic Waves: Peak Topology, Minimal Dynamic Complexity, and Inference for Time-Varying Transmission",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:11Z",
  "url": "https://research.draconitic.com/paper/901yw6y087hbh",
  "venue": "arXiv",
  "versions": []
}