To trace or not to trace: analytical insights from network-based contact-tracing models
Giulia de Meijere, Andrea Pugliese, Gerardo Iñiguez, Péter L Simon, István Z Kiss · 2026
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Description
Giulia de Meijere and co-authors ask when tracing an infected person's contacts actually stops an epidemic, and they remove two comforting assumptions to find out. Models of contact tracing usually pretend tracing runs far faster than the disease spreads, and that every diagnosed case triggers tracing. Real programs enjoy neither luxury. Their 2026 paper gives a full analytical treatment of the epidemic threshold in a network model that keeps both imperfections.
A fast-variables method captures how key network quantities settle early, letting the threshold be computed exactly rather than simulated. The authors also add a new mechanism borrowed from social adoption: a person can be traced through two infected tracing neighbors at once, not just one. For the single-neighbor and double-neighbor versions alike, they derive the critical tracing effort at which an outbreak dies out, formulas health modelers can apply directly.
Catalog record
| CREATOR | Giulia de Meijere, Andrea Pugliese, Gerardo Iñiguez, Péter L Simon, István Z Kiss |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |