Predicting population extinction or disease outbreaks with stochastic models
Author(s) -
Linda J. S. Allen,
Sophia R.J. Jang,
Lih-Ing Roeger
Publication year - 2016
Publication title -
letters in biomathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.506
H-Index - 8
ISSN - 2373-7867
DOI - 10.1080/23737867.2016.1264870
Subject(s) - markov chain , randomness , population , logistic function , infectious disease (medical specialty) , econometrics , exponential growth , extinction (optical mineralogy) , stochastic modelling , computer science , population growth , mathematics , statistics , demography , disease , biology , medicine , sociology , paleontology , mathematical analysis , pathology
Models of exponential growth, logistic growth and epidemics are common applications in undergraduate differential equation courses. The corresponding stochastic models are not part of these courses, although when population sizes are small their behaviour is often more realistic and distinctly different from deterministic models. For example, the randomness associated with births and deaths may lead to population extinction even in an exponentially growing population. Some background in continuous-time Markov chains and applications to populations, epidemics and cancer are presented with a goal to introduce this topic into the undergraduate mathematics curriculum that will encourage further investigation into problems on conservation, infectious diseases and cancer therapy. MATLAB programs for graphing sample paths of stochastic models are provided in the Appendix
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