Characterizing infectious disease progression through discrete states using hidden Markov models

PloS One
Kristina M CeresY T Gröhn

Abstract

Infectious disease management relies on accurate characterization of disease progression so that transmission can be prevented. Slowly progressing infectious diseases can be difficult to characterize because of a latency period between the time an individual is infected and when they show clinical signs of disease. The introduction of Mycobacterium avium ssp. paratuberculosis (MAP), the cause of Johne's disease, onto a dairy farm could be undetected by farmers for years before any animal shows clinical signs of disease. In this time period infected animals may shed thousands of colony forming units. Parameterizing trajectories through disease states from infection to clinical disease can help farmers to develop control programs based on targeting individual disease state, potentially reducing both transmission and production losses due to disease. We suspect that there are two distinct progression pathways; one where animals progress to a high-shedding disease state, and another where animals maintain a low-level of shedding without clinical disease. We fit continuous-time hidden Markov models to multi-year longitudinal fecal sampling data from three US dairy farms, and estimated model parameters using a modified Baum-Welch exp...Continue Reading

References

Jul 1, 1994·Clinical Microbiology Reviews·C CocitoP Vannuffel
Jul 1, 1996·The Veterinary Clinics of North America. Food Animal Practice·R H Whitlock, C Buergelt
Jul 29, 1999·Preventive Veterinary Medicine·S L OttB A Wagner
Jan 7, 2011·Journal of Clinical Microbiology·Abani K PradhanYnte H Schukken
Sep 17, 2013·Preventive Veterinary Medicine·Kumudika de SilvaRichard J Whittington
Jan 15, 2015·Preventive Veterinary Medicine·Rebecca M MitchellYnte H Schukken
Oct 17, 2015·Expert Review of Gastroenterology & Hepatology·Adrienne L McNeesDavid Y Graham
Nov 2, 2015·Preventive Veterinary Medicine·Rebecca L SmithYrjö T Gröhn

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