scholarly journals Population growth and control in stochastic models of cancer development

2004 ◽  
Vol 343 ◽  
pp. 557-572 ◽  
Author(s):  
Anna Ochab-Marcinek ◽  
Ewa Gudowska-Nowak
Keyword(s):  
Population Growth ◽  
Stochastic Models ◽  
Cancer Development ◽  
And Control

scholarly journals New Cancer Stochastic Models Involving Both Hereditary and Nonhereditary Cancer Cases: A New Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-19
Author(s):  
Wai-Yuan Tan ◽  
Hong Zhou
Keyword(s):  
Cancer Incidence ◽  
Stochastic Models ◽  
Biological Information ◽  
Cancer Genes ◽  
New Approach ◽  
Human Cancers ◽  
Seer Data ◽  
Sampling Procedures ◽  
And Control ◽  
Better Than

To incorporate biologically observed epidemics into multistage models of carcinogenesis, in this paper we have developed new stochastic models for human cancers. We have further incorporated genetic segregation of cancer genes into these models to derive generalized mixture models for cancer incidence. Based on these models we have developed a generalized Bayesian approach to estimate the parameters and to predict cancer incidence via Gibbs sampling procedures. We have applied these models to fit and analyze the SEER data of human eye cancers from NCI/NIH. Our results indicate that the models not only provide a logical avenue to incorporate biological information but also fit the data much better than other models. These models would not only provide more insights into human cancers but also would provide useful guidance for its prevention and control and for prediction of future cancer cases.

Demographic factors in attitudes towards population growth and control

1972 ◽  
Vol 4 (1) ◽  
pp. 9-23 ◽  
Author(s):  
Larry D. Barnett
Keyword(s):  
Population Growth ◽  
Family Income ◽  
Household Head ◽  
Demographic Factors ◽  
City Size ◽  
World Population ◽  
Population Limitation ◽  
Business Executive ◽  
World Population Growth ◽  
And Control

Using two Gallup polls, which together contained three questions on the attitudes of adult Americans towards population growth and control, a multivariate analysis was conducted of the relationship to each question of nine demographic factors: age, city size, education, family income, occupation of the household head, race, region, religion and sex. Only education and religion showed an intrinsic relationship with attitudes. Specifically, the extent of endorsement of the view that the world population growth rate is a serious problem, and of the view that population limitation will, at some time, be necessary, increased with education. Among those whose family income was at least $10,000 and those whose house-hold head was a professional or business executive, Protestants were more likely than Catholics to view US and world population growth rates as serious and to consider population limitation necessary.

scholarly journals Estimating the distribution of time to extinction of infectious diseases in mean-field approaches

2020 ◽  
Author(s):  
Maryam Aliee ◽  
Kat S. Rock ◽  
Matt J. Keeling
Keyword(s):  
Infectious Diseases ◽  
Stochastic Models ◽  
Mean Field ◽  
Practical Method ◽  
Deterministic Models ◽  
Recovery Processes ◽  
Time To Extinction ◽  
And Control ◽  
Field Approaches ◽  
Birth Death

AbstractA key challenge for many infectious diseases is to predict the time to extinction under specific interventions. In general this question requires the use of stochastic models which recognise the inherent individual-based, chance-driven nature of the dynamics; yet stochastic models are inherently computationally expensive, especially when parameter uncertainty also needs to be incorporated. Deterministic models are often used for prediction as they are more tractable, however their inability to precisely reach zero infections makes forecasting extinction times problematic. Here, we study the extinction problem in deterministic models with the help of an effective “birth-death” description of infection and recovery processes. We present a practical method to estimate the distribution, and therefore robust means and prediction intervals, of extinction times by calculating their different moments within the birth-death framework. We show these predictions agree very well with the results of stochastic models by analysing the simplified SIS dynamics as well as studying an example of more complex and realistic dynamics accounting for the infection and control of African sleeping sickness (Trypanosoma brucei gambiense).

Stochastic models and control for electrical power line temperature

2015 ◽  
Vol 7 (1) ◽  
pp. 173-192 ◽  
Author(s):  
Daniel Bienstock ◽  
Jose Blanchet ◽  
Juan Li
Keyword(s):  
Stochastic Models ◽  
Electrical Power ◽  
Power Line ◽  
And Control ◽  
Line Temperature
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