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Bayesian hierarchical modeling of respiratory infections in guinea pigs (Cavia porcellus) | ||
| Iraqi Journal of Veterinary Sciences | ||
| Volume 40, Issue 2, April 2026, Pages 429-439 PDF (1001.55 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/ijvs.2026.166768.4595 | ||
| Authors | ||
| Cristian Inca* 1; Franklin Coronel2; Mónica Castelo2; Luis Quishpe2; Evelyn Inca3; Luis Rojas Oviedo2; Patricio Paredes Orozco2; Luis Condo Plaza2; José Carrasco Poma2; Nelly Padilla Padilla2 | ||
| 1Escuela Superior Politécnica de Chimborazo (ESPOCH) | ||
| 2Escuela Superior Politécnica de Chimborazo (ESPOCH) | ||
| 3Research Independent | ||
| Abstract | ||
| Respiratory infections in guinea pigs (Cavia porcellus), a species of high experimental and productive value in the Andean region, represent a frequent health challenge in Ecuador’s highlands. Given their sensitivity to microenvironmental variations, this study aimed to identify environmental and clinical factors associated with the severity of respiratory disease. Data were collected from 50 farms, including variables such as relative humidity, ventilation, temperature, and population density, along with clini-cal indicators of cough, dyspnea, nasal discharge, and lethargy. The information was analyzed using a hierarchical Bayesian spatiotemporal model, which allowed the inte-gration of environmental, physiological, and etiological determinants within a robust inferential framework. The results showed that population density was the main risk factor, exhibiting a posi-tive association with clinical severity. In contrast, adequate ventilation and moderate humidity significantly reduced the frequency of respiratory symptoms. Poor air ex-change favored the accumulation of irritant gases such as ammonia and carbon diox-ide, promoting the spread of Bordetella bronchiseptica. The Bayesian approach overcame the limitations of traditional frequentist methods by providing more accurate estimates of disease severity through the Guinea Pig Respira-tory Clinical Severity Scale (GPRCSS). In conclusion, the hierarchical Bayesian model proved to be an effective predictive tool for sustainable respiratory health manage-ment in high-altitude guinea pig production systems, contributing to both animal wel-fare and the optimization of environmental management practices. | ||
| Keywords | ||
| Andes; Bayesian; guinea pigs; modeling; prediction | ||
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