A Universal Mechanistic Mathematical Model of Parasite Load and Quorum Hijack as Hidden Drivers of ICD-10 Chronic Disease Burden in High-Income Settings

A Universal Mechanistic Mathematical Model of Parasite Load and Quorum Hijack as Hidden Drivers of ICD-10 Chronic Disease Burden in High-Income Settings Authors: Iron Knot Enterprises LLC (Anonymous) Description: This study introduces a universal, falsifiable, mechanistic mathematical framework positing that parasite load serves as a hidden substrate driving a significant subset of chronic disease burdens coded under ICD-10 in high-income countries. Grounded in the One Health paradigm, the model integrates host-parasite dynamics, overdispersed behavioral amplification factors, zoonotic analogues, and parasite-induced immunomodulation to derive human reproduction numbers from livestock parasitology and human epidemiological data. It hypothesizes that modern behavioral patterns—such as sexual contact, communal substance use, and high-density service labor—amplify transmission beyond standard hygiene models, with parasite biomass acting as a universal driver of oncogenic and metabolic risks via a novel “quorum hijack” mechanism. Supported by systematic meta-analyses and case studies documenting disease reversal post-antiparasitic treatment, the model captures equilibria, stability, cancer hazard amplification, and ICD-10 attribution. A field-testable protocol using qPCR, metagenomic screening, and structured reversal validation is proposed, designed for sensitivity analysis and parameter fitting. This work offers testable predictions and actionable public health recommendations, with all data and code available in the description field of the publication platform to ensure reproducibility. The study targets researchers, clinicians, and policymakers to advance understanding and management of neglected parasitic infections. Keywords: Parasite load, quorum hijack, ICD-10, chronic disease, One Health, mathematical modeling, zoonotic diseases, meta-analysis, public health, anthelmintic therapy License: Creative Commons Attribution 4.0 International (CC BY 4.0) Publication Date: September 16, 2025 DOI: 10.5281/zenodo.17136625 Notes: This is Version 3 of the manuscript, ready for peer review. Supplementary materials, including meta-analysis datasets and Python code, will be provided in the description field of the publication platform. Collaborative efforts under One Health frameworks are encouraged to validate the model through multi-site randomized controlled trials.