Deterministic and stochastic infection dynamics in a population subject to stress
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915781171740672 |
|---|---|
| author | Djuikem, Clotilde Arino, Julien |
| author_facet | Djuikem, Clotilde Arino, Julien |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07134 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deterministic and stochastic infection dynamics in a population subject to stress Djuikem, Clotilde Arino, Julien Populations and Evolution Dynamical Systems 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. |
| title | Deterministic and stochastic infection dynamics in a population subject to stress |
| topic | Populations and Evolution Dynamical Systems |
| url | https://arxiv.org/abs/2602.07134 |