# Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations

## TL;DR

This paper models the stochastic fluctuations in SIS epidemic dynamics using Hamiltonian mechanics, revealing how fluctuations influence disease spread and decay, especially in small populations.

## Contribution

It introduces a Hamiltonian framework for the stochastic SIS model, providing new insights into fluctuation effects on epidemic dynamics.

## Key findings

- Hamiltonian is conserved in large populations.
- Finite size effects cause power-law decay near outbreak onset.
- Hamiltonian decay rate indicates epidemic strength.

## Abstract

Empirical records of epidemics reveal that fluctuations are important factors for the spread and prevalence of infectious diseases. The exact manner in which fluctuations affect spreading dynamics remains poorly known. Recent analytical and numerical studies have demonstrated that improved differential equations for mean and variance of infected individuals reproduce certain regimes of the SIS epidemic model. Here, we show they form a dynamical system that follows Hamilton's equations, which allow us to understand the role of fluctuations and their effects on epidemics. Our findings show the Hamiltonian is a constant of motion for large population sizes. For small populations, finite size effects break the temporal symmetry and induce a power-law decay of the Hamiltonian near the outbreak onset, with a parameter-free exponent. Away from onset, the Hamiltonian decays exponentially according to a constant relaxation time, which we propose as a indicator of the strength of the epidemic when fluctuations cannot be neglected.