Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations

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.

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Cited by in corpus (5)

• Unpredictability in seasonal infectious diseases spread
• Exact solutions and superposition rules for Hamiltonian systems generalizing time-dependent SIS epidemic models with stochastic fluctuations
• Quantum-like approaches unveil the intrinsic limits of predictability in compartmental models
• Restoring detailed balance in non-Hermitian Markov processes
• Hamiltonian stochastic Lie systems and applications