Detecting disease progression from animal movement using hidden Markov models
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912604855730176 |
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| author | Kim, Dongmin Michelot, Théo Mertes, Katherine Stabach, Jared A. Fieberg, John |
| author_facet | Kim, Dongmin Michelot, Théo Mertes, Katherine Stabach, Jared A. Fieberg, John |
| contents | Understanding disease dynamics is crucial for managing wildlife populations and assessing spillover risk to domestic animals and humans, but infection data on free-ranging animals are difficult to obtain. Because pathogen and parasite infections can alter host movement, infection status may be inferred from animal trajectories. We present a hidden Markov model (HMM) framework that links observed movement behaviors to unobserved infection states, consistent with epidemiological compartmental models (e.g., susceptible, infected, recovered, dead). Using movement data from 84 reintroduced scimitar-horned oryx (Oryx dammah), 38 confirmed dead in the field and 6 sampled for disease testing, we demonstrate how HMMs can incorporate epidemiological structure through (1) constrained transition probabilities (e.g., to preclude or allow recovery), (2) covariate effects on transmission, and (3) hierarchically structured HMMs (HHMMs) for multi-scale transitions. Comparing veterinary diagnostic reports with model outputs, we found that HMMs with epidemiological constraints successfully identified infection-associated reductions in movement, whereas unconstrained models failed to capture disease progression. Simulations further showed that constrained HMMs accurately classified susceptible, infected, and recovered states. By illustrating flexible formulations and a workflow for model selection, we provide a transferable approach for detecting infection from movement data. This framework can enhance wildlife disease surveillance, guide population management, and improve understanding of disease dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting disease progression from animal movement using hidden Markov models Kim, Dongmin Michelot, Théo Mertes, Katherine Stabach, Jared A. Fieberg, John Quantitative Methods Applications Understanding disease dynamics is crucial for managing wildlife populations and assessing spillover risk to domestic animals and humans, but infection data on free-ranging animals are difficult to obtain. Because pathogen and parasite infections can alter host movement, infection status may be inferred from animal trajectories. We present a hidden Markov model (HMM) framework that links observed movement behaviors to unobserved infection states, consistent with epidemiological compartmental models (e.g., susceptible, infected, recovered, dead). Using movement data from 84 reintroduced scimitar-horned oryx (Oryx dammah), 38 confirmed dead in the field and 6 sampled for disease testing, we demonstrate how HMMs can incorporate epidemiological structure through (1) constrained transition probabilities (e.g., to preclude or allow recovery), (2) covariate effects on transmission, and (3) hierarchically structured HMMs (HHMMs) for multi-scale transitions. Comparing veterinary diagnostic reports with model outputs, we found that HMMs with epidemiological constraints successfully identified infection-associated reductions in movement, whereas unconstrained models failed to capture disease progression. Simulations further showed that constrained HMMs accurately classified susceptible, infected, and recovered states. By illustrating flexible formulations and a workflow for model selection, we provide a transferable approach for detecting infection from movement data. This framework can enhance wildlife disease surveillance, guide population management, and improve understanding of disease dynamics. |
| title | Detecting disease progression from animal movement using hidden Markov models |
| topic | Quantitative Methods Applications |
| url | https://arxiv.org/abs/2509.21132 |