By John Bissell, Camila Caiado, Sarah Curtis, Michael Goldstein, Brian Straughan
This publication specializes in the modelling of latest health and wellbeing and social difficulties, particularly these thought of a huge burden to groups, governments and taxpayers, equivalent to smoking, alcoholism, drug use, and middle illness. according to a chain of papers awarded at a up to date convention hosted by way of the Leverhulme-funded Tipping issues venture on the college of Durham, this booklet illustrates a wide diversity of modelling ways. this sort of various assortment demonstrates that an interdisciplinary technique is vital to modelling tipping issues in overall healthiness and social difficulties, and the review of linked chance and resilience.
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Extra resources for Tipping Points: Modelling Social Problems and Health
Example text
2012 Data from the integrated household survey: smoking prevalence among adults aged 18+ by region and local authority, U. K. id=16678 [last accessed 15th August 2012]. , 1994 Stability, Instability and Chaos: An Introduction to the Theory of Nonlinear Differential Equations, Cambridge, UK, Cambridge University Press. , 2003 Am I too fat? Bulimia as an epidemic, Journal of Mathematical Psychology 47:515–526. Gordon, M. , Nadal, J. , 2009 Discrete choices under social influence: generic properties, Mathematical Models and Methods in Applied Sciences 19:1441–1481.
One could argue that each individual could be represented by their own differential equation increasing the complexity of the deterministic model immensely but the randomness of individual decisions would still be missing. Another possible extension to the deterministic version modelled by differential equations is a system of stochastic differential equations with a similar structure to the original but where the proportions x, y and z in each group are considered random variables. This allows for the introduction of randomness at the group level whilst keeping a similar structure to the deterministic approach.
In the case of the subpopulation of potential smokers, the infection rate ???? is the most important parameter through time regardless if equilibrium has been reached or not, indicating that an increase in ???? at any time could lead to a change in regimen. The basal relapse rate of former smokers a also seem to affect the population of potential smokers. This effect is indirect and mostly due to lag in response from the populations of current and former smokers to the effect of the quitting rate ???? + ???? of current smokers because of the influence of potential smokers; we only compute the Sobol indices at each time point for all parameters, so we do not capture this interaction directly.