# On the Dynamics of Deterministic Epidemic Propagation over Networks

arXiv: https://arxiv.org/abs/1701.03137 · HTML: https://mlsanity.com/paper/1701.03137/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2017.

## Built on by (most cited first, 4 of 4)

- [Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2109.11692/) (2021)
- [Scalable Multi-Agent Reinforcement Learning with General Utilities](https://mlsanity.com/paper/2302.07938/) (2023)
- [Equilibrium Bandits: Learning Optimal Equilibria of Unknown Dynamics](https://mlsanity.com/paper/2302.13653/) (2023)
- [A Physics-Informed Neural Networks-Based Model Predictive Control Framework for SIR Epidemics](https://mlsanity.com/paper/2509.12226/) (2025)

## Abstract (arXiv)

In this work we review a class of deterministic nonlinear models for the propagation of infectious diseases over contact networks with strongly-connected topologies. We consider network models for susceptible-infected (SI), susceptible-infected-susceptible (SIS), and susceptible-infected-recovered (SIR) settings. In each setting, we provide a comprehensive nonlinear analysis of equilibria, stability properties, convergence, monotonicity, positivity, and threshold conditions. For the network SI setting, specific contributions include establishing its equilibria, stability, and positivity properties. For the network SIS setting, we review a well-known deterministic model, provide novel results on the computation and characterization of the endemic state (when the system is above the epidemic threshold), and present alternative proofs for some of its properties. Finally, for the network SIR setting, we propose novel results for transient behavior, threshold conditions, stability properties, and asymptotic convergence. These results are analogous to those well-known for the scalar case. In addition, we provide a novel iterative algorithm to compute the asymptotic state of the network SIR system.