Deterministic and stochastic infection dynamics in a population subject to stress

TL;DR

This paper models how physiological stress affects disease spread in aquatic populations, revealing that stochastic factors can cause outbreaks even when deterministic models predict stability.

Contribution

It introduces a stress-structured epidemiological model incorporating water quality effects and analyzes both deterministic and stochastic dynamics, highlighting asymmetries in outbreak probabilities.

Findings

Deterministic model shows a forward bifurcation at R0=1.

Stochastic analysis uncovers asymmetries in outbreak likelihood.

Initial physiological state influences outbreak probability.

Abstract

Physiological stress fundamentally alters disease susceptibility in aquatic environments. In this paper, we develop a stress-structured epidemiological model where host vulnerability is dynamically driven by water quality. Analytically, we establish that the system exhibits a classic forward bifurcation at $\mathcal{R}_0=1$, confirming that the basic reproduction number remains a valid threshold for eradication. However, stochastic analysis reveals a critical asymmetry not captured by deterministic thresholds. We show that while $\mathcal{R}_0$ predicts stability, the probability of an outbreak depends on the initial physiological state. Introducing infection into a stressed sub-population leads to immediate rapid growth of the disease, whereas introduction into the normal class faces a stochastic barrier that significantly delays the epidemic peak.