Transition from Susceptible-Infected to Susceptible-Infected-Recovered Dynamics in a Susceptible-Cleric-Zombie-Recovered Active Matter Model
A. Libal, P. Forgacs, A. Neda, C. Reichhardt, N. Hengartner, and, C.J.O. Reichhardt

TL;DR
This paper introduces the SCZR active matter epidemic model, which interpolates between SI and SIR dynamics by incorporating clerics and zombies, capturing complex recovery and infection processes relevant to real diseases.
Contribution
The novel SCZR model extends epidemic modeling by including clerics and zombie interactions, allowing transition between SI and SIR behaviors based on initial conditions and healing rates.
Findings
Model can switch between SI and SIR dynamics.
Interaction probabilities influence long-term disease outcomes.
Applicable to diseases like HIV with limited recovery options.
Abstract
The Susceptible-Infected (SI) and Susceptible-Infected-Recovered (SIR) models provide two distinct representations of epidemic evolution, distinguished by the lack of spontaneous recovery in the SI model. Here we introduce a new active matter epidemic model, the ``Susceptible-Cleric-Zombie-Recovered'' (SCZR) model, in which spontaneous recovery is absent but zombies can recover with probability via interaction with a cleric. Upon interacting with a zombie, both susceptibles and clerics can enter the zombie state with probability and , respectively. By changing the intial fraction of clerics or their healing ability rate , we can tune the SCZR model between SI dynamics, in which no susceptibles or clerics remain at long times, and SIR dynamics, in which no zombies remain at long times. The model is relevant to certain real world diseases such as HIV where…
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.
Taxonomy
TopicsEvolutionary Game Theory and Cooperation · Complex Network Analysis Techniques · COVID-19 epidemiological studies
