Characterizing infectious disease progression through discrete states using hidden Markov models
Kristina M Ceres1, Ynte H Schukken2, Yrjö T Gröhn1
1Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States of America.
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 expectation maximization algorithm. Using posterior decoding, we observed two distinct shedding patterns: cows that had observations associated with a high-shedding disease state, and cows that did not. This model framework can be employed prospectively to determine which cows are likely to progress to clinical disease and may be applied to characterize disease progression of other slowly progressing infectious diseases.
Insights
Detecting Johne's disease early is crucial for dairy farms. This study identified two distinct Mycobacterium avium ssp. paratuberculosis (MAP) shedding patterns in cows, aiding in early detection and control of this slow-progressing infectious disease.
Area of Science:
- Veterinary epidemiology
- Infectious disease modeling
- Dairy cattle health
Background:
- Accurate characterization of infectious disease progression is vital for effective management and transmission prevention in livestock.
- Slowly progressing diseases, like Johne's disease caused by Mycobacterium avium ssp. paratuberculosis (MAP), present diagnostic challenges due to long latency periods.
- Undetected MAP infection on dairy farms can lead to significant animal shedding and production losses before clinical signs appear.
Purpose of the Study:
- To parameterize disease progression trajectories for Mycobacterium avium ssp. paratuberculosis (MAP) in dairy cattle.
- To identify distinct disease progression pathways, specifically differentiating between high-shedding and low-shedding states.
- To develop a model framework for early detection and targeted control of Johne's disease.
Main Methods:
- Utilized multi-year longitudinal fecal sampling data from three US dairy farms.
- Applied continuous-time hidden Markov models to analyze disease progression.
- Employed a modified Baum-Welch expectation maximization algorithm for parameter estimation and posterior decoding to identify shedding patterns.
Main Results:
- Identified two distinct fecal shedding patterns in dairy cows associated with Mycobacterium avium ssp. paratuberculosis (MAP) infection.
- Observed distinct disease states, including a high-shedding state and a low-shedding state without apparent clinical disease.
- The model successfully differentiated between cows exhibiting high shedding and those without.
Conclusions:
- The developed model framework can distinguish between different Mycobacterium avium ssp. paratuberculosis (MAP) shedding patterns in dairy cattle.
- This approach enables prospective identification of cows likely to progress to clinical Johne's disease.
- The model framework is applicable to characterizing the progression of other slowly progressing infectious diseases in livestock.
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