{
  "abstract": "The basic reproduction number, $R_0$, is central to infectious-disease modelling, yet analytic expressions can be difficult to derive for high-dimensional, nonlinear, or structurally complex models. We introduce REEBLRA ($R_0$ Equation Estimation using Bayesian Logistic Regression Algorithm), an interpretable framework that learns explicit surrogate equations for epidemic thresholds from parameter--response data. Rather than directly regressing $R_0$, REEBLRA estimates $\\Pr\\{R_0(\\boldsymbolθ)>c\\}$ for parameter vector $\\boldsymbolθ$ and threshold $c$. It provides a binary equation for classifying epidemic invasion ($R_0>1$) and successive threshold equations defining ordered brackets that approximate $R_0$, the expected number of secondary infections caused by a typical case in a fully susceptible population. The resulting equations support parameter screening, sensitivity interpretation, threshold classification, and exploratory analysis when analytic derivation is cumbersome. Bayesian logistic regression using standardized polynomial-like features and proper weakly informative priors yields regularized estimates, posterior uncertainty, and finite coefficients under complete or quasi-complete separation. We establish a mechanistic--surrogate concordance principle linking the full fitted-score gradient to the local gradient of $\\log R_0$ under explicit regularity and tangency assumptions, allowing conditional interpretation of threshold directions. Across seven analytic-label benchmarks associated with ODE models, including a 15-compartment five-group SIR system, five-fold binary AUC was 0.999--1.000 and ordered-bracket accuracy was 0.951--0.998. REEBLRA complements rather than replaces mechanistic derivations, bridging simulation, machine learning, and reproduction-number analysis. An accompanying Shiny app supports implementation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.29399",
  "author_total": 5,
  "authors": [
    {
      "name": "Jomar F. Rabajante"
    },
    {
      "name": "Norvin P. Bansilan"
    },
    {
      "name": "Maria Czarina T. Lagura-Arreza"
    },
    {
      "name": "Destiny SM. Lutero"
    },
    {
      "name": "Emerson R. Rico"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fgxeexd03yqt9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.29399",
  "fulltext_url": null,
  "id": "fgxeexd03yqt9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.29399",
  "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"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2739|Mathematical and Theoretical Epidemiology and Ecology Models"
    ]
  ],
  "openalex": "W7223200176",
  "pdf_url": "https://arxiv.org/pdf/2610.29399",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fgxeexd03yqt9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:23Z",
      "native_id": "2610.29399",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.29399"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "biology",
      "origin": "arxiv:q-bio.PE",
      "subfield": "evolutionary-biology"
    }
  ],
  "title": "Learning Epidemic Threshold Equations from Simulation: REEBLRA, an Interpretable Bayesian Classification Framework for Reproduction Numbers",
  "title_text": "Learning Epidemic Threshold Equations from Simulation: REEBLRA, an Interpretable Bayesian Classification Framework for Reproduction Numbers",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:23Z",
  "url": "https://research.draconitic.com/paper/fgxeexd03yqt9",
  "venue": "arXiv",
  "versions": []
}