Watanabe2018_Simple markov model
Simple Markov model. There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: 0.01; Healthy to Sick: 0.2 for Male and 0.1 for Female; Sick to Healthy: 0.1; Sick to Dead: 0.3. The transition probability now depends on the cohort (Male or Female) and can be expressed as a function of a Boolean covariate Male. Initial conditions: Healthy = (50 Male, 50 Female), Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10.
- Toward reproducible disease models using the Systems Biology Markup Language
- Leandro Watanabe, Jacob Barhak, Chris Myers
- SAGE journals , 9/ 2018
- Department of Electrical and Computer Engineering, University of Utah, USA 2Jacob Barhak, Austin, TX, USA
- Disease modelers have been modeling progression of diseases for several decades using such tools as Markov models or microsimulation. However, they need to address a serious challenge; many models they create are not reproducible. Moreover, there is no proper practice that ensures reproducible models, since modelers rely on loose guidelines that change periodically, rather than well-defined machine-readable standards. The Systems Biology Markup Language (SBML) is one such standard that allows exchange of models between different software tools. Recently, the SBML Arrays package has been developed, which extends the standard to allow handling simulation of populations. This paper demonstrates through several abstract examples how microsimulation disease models can be encoded using the SBML Arrays package, enabling reproducible disease modeling.
Leandro Watanabe, Rahuman Sheriff
|Example3.xml||SBML level 3 version 2, representation of Example3 - Stratified Markov Model||55.17 KB||Preview | Download|