This book analyses the development and use of mathematical models in public health research and policy. By introducing a life cycle metaphor, the author provides a unique perspective on how mathematical modelling techniques have increased our understanding of the governance of infectious risks in society.
This book analyses the development and use of mathematical models in public health research and policy. By introducing a life cycle metaphor, the author provides a unique perspective on how mathematical modelling techniques have increased our understanding of the governance of infectious risks in society.
Erika Mansnerus is Research Fellow at the London School of Economics and Political Science, UK, where she teaches sociology and social policy. Her work has been funded by the British Academy, the Economic and Social Research Council (ESRC) and the Department for Food, Environment and Rural Affairs, UK. She was awarded her PhD at the University of Helsinki in 2007.
Inhaltsangabe
1. Introduction: Life-Cycles Of Models 2. Models And The Stories They Tell Us 2.1 Introduction 2.2 Story Of Disease Transmission 2.3 Telling Policy-Relevant Stories 2.4 Conclusion 3. Kinship Relations Of Models 3.1 Models And Kinship Ties 3.2 Kinship: Evolving Relations And Links Between Models 3.3 Nature Of Model-Based Evidence 3.4 Conclusion 4. Working Lives Of Models 4.1 Introduction 4.2 Revising Vaccination Policy: From Target Groups To Mass Campaign 4.3 Insights Into Models At Work 4.4 Conclusion 5. Encounters With Risks 5.1 Introduction 5.2 Two Types Of Model-Based Predictions 5.3 Using Models In Risk Assessment: Simulating Pandemic Outbreaks 5.4 Conclusion 6. When Evidence Is Silent 6.1 Introduction: Silent Evidence Of A Pandemic Influenza 6.2 What Is Meant By Silent Evidence? 6.3 Identifying Known Unknowns 6.4 Analysing Unknown Factors 6.5 Conclusion 7. Governing By Numbers 7.1 Introduction 7.2 Governing By Numbers 7.3 Using Models As Senior Experts 7.4 Quantitative Authority Of Modelling Of Foot-And-Mouth Disease (FMD) 7.5 Conclusion 8. Lives Of Models In The World Of Policy 8.1 Understanding Models Through Their Lives 8.2 Predicting, Preventing And Keeping Us Healthy 8.3 Benefits And Limitations Of Modelling Techniques 8.4 Conclusions
1. Introduction: Life-Cycles Of Models 2. Models And The Stories They Tell Us 2.1 Introduction 2.2 Story Of Disease Transmission 2.3 Telling Policy-Relevant Stories 2.4 Conclusion 3. Kinship Relations Of Models 3.1 Models And Kinship Ties 3.2 Kinship: Evolving Relations And Links Between Models 3.3 Nature Of Model-Based Evidence 3.4 Conclusion 4. Working Lives Of Models 4.1 Introduction 4.2 Revising Vaccination Policy: From Target Groups To Mass Campaign 4.3 Insights Into Models At Work 4.4 Conclusion 5. Encounters With Risks 5.1 Introduction 5.2 Two Types Of Model-Based Predictions 5.3 Using Models In Risk Assessment: Simulating Pandemic Outbreaks 5.4 Conclusion 6. When Evidence Is Silent 6.1 Introduction: Silent Evidence Of A Pandemic Influenza 6.2 What Is Meant By Silent Evidence? 6.3 Identifying Known Unknowns 6.4 Analysing Unknown Factors 6.5 Conclusion 7. Governing By Numbers 7.1 Introduction 7.2 Governing By Numbers 7.3 Using Models As Senior Experts 7.4 Quantitative Authority Of Modelling Of Foot-And-Mouth Disease (FMD) 7.5 Conclusion 8. Lives Of Models In The World Of Policy 8.1 Understanding Models Through Their Lives 8.2 Predicting, Preventing And Keeping Us Healthy 8.3 Benefits And Limitations Of Modelling Techniques 8.4 Conclusions
1. Introduction: Life-Cycles Of Models 2. Models And The Stories They Tell Us 2.1 Introduction 2.2 Story Of Disease Transmission 2.3 Telling Policy-Relevant Stories 2.4 Conclusion 3. Kinship Relations Of Models 3.1 Models And Kinship Ties 3.2 Kinship: Evolving Relations And Links Between Models 3.3 Nature Of Model-Based Evidence 3.4 Conclusion 4. Working Lives Of Models 4.1 Introduction 4.2 Revising Vaccination Policy: From Target Groups To Mass Campaign 4.3 Insights Into Models At Work 4.4 Conclusion 5. Encounters With Risks 5.1 Introduction 5.2 Two Types Of Model-Based Predictions 5.3 Using Models In Risk Assessment: Simulating Pandemic Outbreaks 5.4 Conclusion 6. When Evidence Is Silent 6.1 Introduction: Silent Evidence Of A Pandemic Influenza 6.2 What Is Meant By Silent Evidence? 6.3 Identifying Known Unknowns 6.4 Analysing Unknown Factors 6.5 Conclusion 7. Governing By Numbers 7.1 Introduction 7.2 Governing By Numbers 7.3 Using Models As Senior Experts 7.4 Quantitative Authority Of Modelling Of Foot-And-Mouth Disease (FMD) 7.5 Conclusion 8. Lives Of Models In The World Of Policy 8.1 Understanding Models Through Their Lives 8.2 Predicting, Preventing And Keeping Us Healthy 8.3 Benefits And Limitations Of Modelling Techniques 8.4 Conclusions
1. Introduction: Life-Cycles Of Models 2. Models And The Stories They Tell Us 2.1 Introduction 2.2 Story Of Disease Transmission 2.3 Telling Policy-Relevant Stories 2.4 Conclusion 3. Kinship Relations Of Models 3.1 Models And Kinship Ties 3.2 Kinship: Evolving Relations And Links Between Models 3.3 Nature Of Model-Based Evidence 3.4 Conclusion 4. Working Lives Of Models 4.1 Introduction 4.2 Revising Vaccination Policy: From Target Groups To Mass Campaign 4.3 Insights Into Models At Work 4.4 Conclusion 5. Encounters With Risks 5.1 Introduction 5.2 Two Types Of Model-Based Predictions 5.3 Using Models In Risk Assessment: Simulating Pandemic Outbreaks 5.4 Conclusion 6. When Evidence Is Silent 6.1 Introduction: Silent Evidence Of A Pandemic Influenza 6.2 What Is Meant By Silent Evidence? 6.3 Identifying Known Unknowns 6.4 Analysing Unknown Factors 6.5 Conclusion 7. Governing By Numbers 7.1 Introduction 7.2 Governing By Numbers 7.3 Using Models As Senior Experts 7.4 Quantitative Authority Of Modelling Of Foot-And-Mouth Disease (FMD) 7.5 Conclusion 8. Lives Of Models In The World Of Policy 8.1 Understanding Models Through Their Lives 8.2 Predicting, Preventing And Keeping Us Healthy 8.3 Benefits And Limitations Of Modelling Techniques 8.4 Conclusions
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