On the Similarity between Epidemiologic Strains, Minimal Self-Replicable Siphons, and autocatalytic cores in (Chemical) Reaction Networks: Towards a Unifying Framework
Florin Avram, Rim Adenane, Lasko Basnarkov, Andras Horvath

TL;DR
This paper explores the structural similarities between epidemiological models and chemical reaction networks, proposing a unifying framework that links disease dynamics with autocatalytic core concepts, and introduces algorithms for analysis.
Contribution
It establishes conceptual correspondences between epidemiologic strains and chemical reaction network cores, and develops a unifying framework with algorithms for analyzing model structures.
Findings
Epidemiologic strains correspond to minimal siphons and autocatalytic cores in CRNs.
Models with acyclic siphon interaction graphs exhibit block triangular NGMs.
Algorithms for detecting siphons and AMSD are implemented in the Epid-CRN package.
Abstract
We aim to study boundary stability and persistence of positive odes in mathematical epidemiology models by importing structural tools from chemical reaction networks. This is largely a review work, which attempts to bring closer together the fields of mathematical epidemiology (ME), and chemical reaction networks (CRNs), based on several observations. We started by observing the conceptual correspondence between epidemiologic strains and both critical minimal siphons and minimal autocatalytic sets (cores) in an underlying CRN, and confirmed this in all the models we studied. We leverage this to provide a definition of the disease free equilibrium (DFE) face/infected set as the union of either all minimal siphons, or of all cores (they coincide always in our examples). Next, we provide a proposed definition of ME models, as models which have a unique boundary fixed point on the DFE face,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
