Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
Abstract
A spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mice and our model has been obtained.
Main Authors
Format
Articles
Research article
Published
2020
Series
Subjects
Publication in research information system
Publisher
Springer
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202002192114Käytä tätä linkitykseen.
Review status
Peer reviewed
ISSN
0303-6812
DOI
https://doi.org/10.1007/s00285-019-01367-y
Language
English
Published in
Journal of Mathematical Biology
Citation
- Vermolen, F., & Pölönen, I. (2020). Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer. Journal of Mathematical Biology, 80(3), 545-573. https://doi.org/10.1007/s00285-019-01367-y
License
Copyright© The Authors 2019