A1 Journal article (refereed)
Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer (2020)


Vermolen, Fred; Pölönen, Ilkka (2020). Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer. Journal of Mathematical Biology, 80 (3), 545-573. DOI: 10.1007/s00285-019-01367-y


JYU authors or editors


Publication details

All authors or editors: Vermolen, Fred; Pölönen, Ilkka

Journal or series: Journal of Mathematical Biology

ISSN: 0303-6812

eISSN: 1432-1416

Publication year: 2020

Volume: 80

Issue number: 3

Pages range: 545-573

Publisher: Springer

Publication country: Germany

Publication language: English

DOI: http://doi.org/10.1007/s00285-019-01367-y

Open Access: Open access publication published in a hybrid channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67880

Publication is parallel published: https://repository.tudelft.nl/islandora/object/uuid:ee4c59e6-b6bc-4efe-bc19-6ee3f29f69e7


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.


Keywords: mathematical models; stochastic processes; Markov chains; skin cancer


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 3


Last updated on 2020-18-08 at 13:09