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E-raamat: Tipping Points: Modelling Social Problems and Health

Edited by (University of Durham, UK), Edited by (University of Durham, UK), Edited by (University of Durham, UK), Edited by (University of Durham, UK), Edited by (University of Durham, UK)
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This book focuses on the modelling of contemporary health and social problems, especially those considered a major burden to communities, governments and taxpayers, such as smoking, alcoholism, drug use, and heart disease. Based on a series of papers presented at a recent conference hosted by the Leverhulme-funded Tipping Points project at the University of Durham, this book illustrates a broad range of modelling approaches. Such a diverse collection demonstrates that an interdisciplinary approach is essential to modelling tipping points in health and social problems, and the assessment of associated risk and resilience.

List of Contributors xi
Acknowledgements xiii
Introduction xv
Part I The Smoking Epidemic 1(48)
1 Generalised Compartmental Modelling of Health Epidemics
3(18)
1.1 Introduction
3(2)
1.2 Basic compartmental model of smoking dynamics
5(3)
1.3 Properties of the basic model
8(2)
1.3.1 Steady-state solutions
8(1)
1.3.2 Steady-state stability
9(1)
1.4 Generalised model inclusive of multiple peer recruitment
10(5)
1.4.1 Smoking-free equilibrium in the generalised model
12(1)
1.4.2 New smoking-present equilibria in the generalised model
13(2)
1.5 Bistability and 'tipping points' in the generalised model
15(3)
1.5.1 Steady-state variation with c
15(2)
1.5.2 'Tipping points' and hysteresis
17(1)
1.6 Summary and conclusions
18(1)
Acknowledgements
19(1)
References
19(2)
2 Stochastic Modelling for Compartmental Systems Applied to Social Problems
21(11)
2.1 Introduction
21(2)
2.2 Global sensitivity analysis of deterministic models
23(1)
2.3 Sensitivity analysis of the generalised smoking model with peer influence
24(2)
2.4 Adding randomness to a deterministic model
26(2)
2.5 Sensitivity analysis of the stochastic analogue
28(2)
2.6 Conclusion
30(1)
Acknowledgements
31(1)
References
31(1)
3 Women and Smoking in the North East of England
32(17)
3.1 Introduction
33(1)
3.2 Background
33(2)
3.3 Interrogating the figures
35(4)
3.4 Materialist and cultural or behavioural explanations
39(2)
3.5 The tobacco industry and the creation of social values
41(2)
3.6 Local voices
43(1)
3.7 Conclusions
44(1)
Acknowledgements
45(1)
References
45(4)
Part II Mathematical Modelling In Healthcare 49(64)
4 Cardiac Surgery Performance Monitoring
51(31)
4.1 Introduction
52(3)
4.1.1 Why do we monitor cardiac surgery providers?
53(1)
4.1.2 Professional framework for monitoring
53(1)
4.1.3 Objectives
54(1)
4.2 Statistical framework for monitoring
55(6)
4.2.1 Data collection
55(1)
4.2.2 Data extraction and cleaning
55(1)
4.2.3 Missing data and imputation
56(1)
4.2.4 Risk adjustment
56(1)
4.2.5 Risk-adjustment methodology
57(1)
4.2.6 The status quo
58(1)
4.2.7 Measuring divergence
58(3)
4.3 A non-stationary process
61(7)
4.3.1 Calibration drift
62(1)
4.3.2 Discrimination
63(1)
4.3.3 A changing population
64(4)
4.3.4 A closer inspection of calibration
68(1)
4.4 Dynamic modelling approaches
68(6)
4.4.1 Model approaches
68(4)
4.4.2 Comparison of model approaches
72(2)
4.5 Case example
74(1)
4.6 Discussion
75(2)
4.7 Conclusion
77(1)
Acknowledgements
78(1)
References
78(4)
5 Heart Online Uncertainty and Stability Estimation
82(13)
5.1 Introduction
83(1)
5.2 Monitoring live complex systems
83(2)
5.3 The Bayes linear approach
85(1)
5.4 The Fantasia and Sudden Cardiac Death databases
86(1)
5.5 Exploring ECG datasets
87(4)
5.6 Assessing discrepancy
91(2)
5.7 Final remarks and conclusion
93(1)
Acknowledgements
93(1)
References
94(1)
6 Stents, Blood Flow and Pregnancy
95(18)
6.1 Introduction
96(1)
6.2 Drug-eluting stents
97(4)
6.2.1 Mathematical model
97(2)
6.2.2 Modelling drug release
99(1)
6.2.3 Modelling the coupled problem
99(1)
6.2.4 Solving the model equations
100(1)
6.2.5 Remarks on modelling drug release
100(1)
6.3 Modelling blood flow
101(2)
6.3.1 Mathematical model of blood flow
101(2)
6.3.2 Application to blood flow in a dog's femoral artery
103(1)
6.4 Modelling a capillary-fill medical diagnostic tool
103(7)
6.4.1 Basic equations
105(4)
6.4.2 Recharacterisation of the model
109(1)
6.4.3 Comments
110(1)
6.5 Summary and closing remarks
110(1)
References
111(2)
Part III Tipping Points In Social Dynamics 113(70)
7 From Five Key Questions to a System Sociology Theory
115(15)
7.1 Introduction
116(1)
7.2 Complexity features
117(2)
7.3 Mathematical tools
119(3)
7.4 Black Swans from the interplay of different dynamics
122(3)
7.4.1 Nature of the interactions
123(1)
7.4.2 Generator of a BS
124(1)
7.4.3 Domino effect
125(1)
7.5 Validation of models
125(1)
7.6 Conclusions: towards a mathematical theory of social systems
126(1)
Acknowledgments
127(1)
References
127(3)
8 Complexity in Spatial Dynamics: The Emergence of Homogeneity/Heterogeneity in Culture in Cities
130(16)
8.1 Introduction
131(1)
8.2 Modelling approach
132(2)
8.3 Description of the model
134(4)
8.4 Sensitivity analysis and results
138(3)
8.5 Discussion and conclusions
141(2)
Acknowledgements
143(1)
References
143(3)
9 Cultural Evolution, Gene-Culture Coevolution, and Human Health
146(22)
9.1 Introduction
147(2)
9.2 Cultural evolution
149(4)
9.2.1 Self-medication treatment efficacy
150(3)
9.3 Epidemiological modelling of cultural change
153(4)
9.3.1 Drinking behaviour
154(3)
9.4 Gene-culture coevolution
157(6)
9.4.1 Lactase persistence and dairying
160(3)
9.5 Conclusion
163(1)
References
164(4)
10 Conformity Bias and Catastrophic Social Change
168(15)
10.1 Introduction
168(3)
10.2 Three-population compartmental model
171(2)
10.3 Basic system excluding conformity bias
173(1)
10.4 Including conformity bias
174(2)
10.5 Comparative statics
176(2)
10.6 Summary
178(1)
10.7 Conclusions
179(1)
Acknowledgements
180(1)
Appendix 10.A: Stability in the conformity bias model
180(1)
References
181(2)
Part IV The Resilience Of Tipping Points 183(26)
11 Psychological Perspectives on Risk and Resilience
185(11)
11.1 Introduction
185(1)
11.2 Forensic psychological risk assessments in prisons
186(1)
11.3 Suicide in prisons
187(2)
11.4 Biases in human decision making - forensic psychologists making risky decisions
189(3)
11.5 The Port of London Authority
192(2)
11.6 Final thoughts and reflections
194(1)
Acknowledgements
194(1)
References
194(2)
12 Tipping Points and Uncertainty in Health and Healthcare Systems
196(13)
12.1 Introduction: 'tipping points' as 'critical events' in health systems
197(1)
12.2 Prediction, prevention and preparedness strategies for risk resilience in complex systems
198(2)
12.3 No such thing as a 'never event'?
200(2)
12.4 Local versus large-scale responses to risk
202(2)
12.5 Conclusions: the ongoing agenda for research on tipping points in complex systems
204(1)
Endnotes and acknowledgements
205(1)
References
205(4)
Index 209
J. J. Bissell, Department of Mathematical Sciences, University of Durham, UK.

C. C. S. Caiado, Department of Mathematical Sciences, University of Durham, UK.

S. E. Curtis, Institute of Hazard, Risk and Resilience, University of Durham, UK.

M. Goldstein, Department of Mathematical Sciences, University of Durham, UK.

Brian Straughan, Department of Mathematical Sciences, University of Durham, UK.
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