Spatial Agent-Based Simulation Modeling in Public Health
Design, Implementation, and Applications for Malaria Epidemiology
Published on 27. May 2016
Book
Hardback
320 pages
978-1-118-96435-4 (ISBN)
Description
Presents an overview of the complex biological systems used within a global public health setting and features a focus on malaria analysis
Bridging the gap between agent-based modeling and simulation (ABMS) and geographic information systems (GIS), Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology provides a useful introduction to the development of agent-based models (ABMs) by following a conceptual and biological core model of Anopheles gambiae for malaria epidemiology. Using spatial ABMs, the book includes mosquito (vector) control interventions and GIS as two example applications of ABMs, as well as a brief description of epidemiology modeling. In addition, the authors discuss how to most effectively integrate spatial ABMs with a GIS. The book concludes with a combination of knowledge from entomological, epidemiological, simulation-based, and geo-spatial domains in order to identify and analyze relationships between various transmission variables of the disease.
Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology also features:
* Location-specific mosquito abundance maps that play an important role in malaria control activities by guiding future resource allocation for malaria control and identifying hotspots for further investigation
* Discussions on the best modeling practices in an effort to achieve improved efficacy, cost-effectiveness, ecological soundness, and sustainability of vector control for malaria
* An overview of the various ABMs, GIS, and spatial statistical methods used in entomological and epidemiological studies, as well as the model malaria study
* A companion website with computer source code and flowcharts of the spatial ABM and a landscape generator tool that can simulate landscapes with varying spatial heterogeneity of different types of resources including aquatic habitats and houses
Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology is an excellent reference for professionals such as modeling and simulation experts, GIS experts, spatial analysts, mathematicians, statisticians, epidemiologists, health policy makers, as well as researchers and scientists who use, manage, or analyze infectious disease data and/or infectious disease-related projects. The book is also ideal for graduate-level courses in modeling and simulation, bioinformatics, biostatistics, public health and policy, and epidemiology.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 22 mm
Weight
651 gr
ISBN-13
978-1-118-96435-4 (9781118964354)
Schweitzer Classification
Other editions
Additional editions
S. M. Niaz Arifin | Gregory R. Madey | Frank H. Collins
Spatial Agent-Based Simulation Modeling in Public Health
Design, Implementation, and Applications for Malaria Epidemiology
E-Book
03/2016
Wiley
€113.99
Available for download
S. M. Niaz Arifin | Gregory R. Madey | Frank H. Collins
Spatial Agent-Based Simulation Modeling in Public Health
Design, Implementation, and Applications for Malaria Epidemiology
E-Book
03/2016
Wiley
€113.99
Available for download
Persons
S. M. Niaz Arifin, PhD, is Research Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame. A member of The Society for Computer Simulation and American Society of Tropical Medicine and Hygiene and the recipient of The American Society of Tropical Medicine and Hygiene Travel Award in 2011, his research interests include agent-based modeling and simulation, public health, data warehousing, and geographic information systems.
Gregory R. Madey, PhD, is Research Professor in the Department of Computer Science and Engineering at the University of Notre Dame. A member of The Society for Computer Simulation, Institute of Electrical and Electronics Engineers Computer Society, and American Society of Tropical Medicine and Hygiene, his research interests include agent-based modeling and simulation, cyberinfrastructure, bioinformatics, biocomplexity, e-Technologies, open source software, disaster management, and health informatics.
Frank H. Collins, PhD, is Professor in the Department of Biological Sciences at the University of Notre Dame. His research interests include genome level studies of arthropod vectors of human pathogens, the biology of malaria vectors with a focus on the development of molecular tools that will permit better resolution of questions about vector population ecology, ecological genetics, and the epidemiology of malaria transmission.
Content
List of Contributors xv
List of Figures xvii
List of Tables xxi
Preface xxiii
Acknowledgements xxix
List of Abbreviations xxxi
1 Introduction 1
1.1 Overview 1
1.2 Malaria 3
1.3 Agent-Based Modeling of Malaria 4
1.4 Contributions 4
1.5 Organization 5
2 Malaria: A Brief History 7
2.1 Overview 7
2.2 Malaria in Human History 7
2.2.1 The Malarial Path: Ancient Origins 8
2.2.2 Naming and Key Discoveries 9
2.2.3 Antimalarial Drugs 9
2.2.4 Prevention Measures 10
2.3 Malaria Epidemiology: A Global View 10
2.3.1 The Malaria Parasite 11
2.3.2 Geographic Distribution 12
2.3.3 Types of Transmission 12
2.3.4 Risk Mapping and Forecasting 13
2.4 Malaria Control 13
3 Agent-Based Modeling and Malaria 17
3.1 Overview 17
3.2 Agent-Based Models (ABMs) 17
3.2.1 Agents 18
3.2.2 Environment 19
3.2.3 Rules 20
3.2.4 Software for ABMs 20
3.3 History and Applications 21
3.3.1 M&S Organizations 21
3.4 Advantages of ABMs 23
3.4.1 Emergence, Aggregation, and Complexity 23
3.4.2 Heterogeneity 24
3.4.3 Learning and Adaptation 24
3.4.4 Flexibility in System Description 24
3.4.5 Inclusion of Multiple Spaces 25
3.4.6 Limitations of ABMs 25
3.4.7 ABMs vs Mathematical Models 27
3.4.8 Applicability of ABMs for Malaria Modeling 28
3.5 Malaria Models: A Review 29
3.5.1 Mathematical Models of Malaria 30
3.5.2 Agent-Based Models (ABMs) of Malaria 33
3.5.3 The Spatial Dimension of Malaria Models 35
3.6 Summary 36
4 The Biological Core Model 39
4.1 Overview 39
4.1.1 Relevant Terms of Interest 40
4.2 The Aquatic Phase 41
4.2.1 Egg (E) 42
4.2.2 Larva (L) 43
4.2.3 Pupa (P) 45
4.3 The Adult Phase 46
4.3.1 Immature Adult (IA) 46
4.3.2 Mate Seeking (MS) 47
4.3.3 Blood Meal Seeking (BMS) 47
4.3.4 Blood Meal Digesting (BMD) 47
4.3.5 Gravid (G) 47
4.4 Aquatic Habitats and Oviposition 48
4.4.1 Aquatic Habitats 48
4.4.2 Oviposition 48
4.5 Senescence and Mortality Rates 50
4.5.1 Senescence 50
4.5.2 Mortality Models: Basic Mathematical Formulation 51
4.6 Mortality in the Core Model 51
4.6.1 Aquatic (Immature) Mortality Rates 52
4.6.2 Adult Mortality Rates 53
4.7 Discussion 53
4.7.1 An Extendible Framework for Other Anopheline Species 53
4.7.2 Weather, Seasonality, and Other Factors 54
4.7.3 Mortality Rates 54
4.8 Summary 54
5 The Agent-Based Model (ABM) 57
5.1 Overview 57
5.2 Model Architecture 58
5.2.1 Object-Oriented Programming (OOP) Terminology 58
5.2.2 Agents 60
5.2.3 Environments 62
5.2.4 Event-Action-List Diagram 62
5.3 Mosquito Population Dynamics 64
5.4 Model Features 66
5.4.1 Processing Steps Ordering 66
5.4.2 Model Assumptions 67
5.4.3 Simulations 69
5.5 Summary 69
6 The Spatial ABM 71
6.1 Overview 71
6.2 The Spatial ABM 74
6.2.1 Definition of Terms 74
6.2.2 Landscapes 75
6.2.3 Landscape Generator Tools 76
6.3 Resource Clustering 79
6.4 Flight Heuristics for Mosquito Agents 81
6.5 Simulation Results 85
6.5.1 Model Verification 85
6.5.2 Landscape Patterns 86
6.5.3 Relative Sizes of Resources 87
6.5.4 Resource Density 88
6.5.5 Combined Habitat Capacity (CHC) 89
6.6 Spatial Heterogeneity 90
6.7 Summary 93
7 Verification, Validation, Replication, and Reproducibility 95
7.1 Overview 95
7.2 Verification and Validation (V&V): A Review 96
7.2.1 Acceptability Assessment and Quality Assurance (QA) 96
7.2.2 Verification and Validation (V&V) 98
7.3 Replication and Reproducibility (R&R): A Review 100
7.4 Summary 101
8 Verification and Validation (V&V) of ABMs 103
8.1 Overview 103
8.2 Verification and Validation (V&V) of ABMs 103
8.3 Phase-Wise Docking 105
8.3.1 Assumptions for the ABMs 105
8.3.2 Phase-Wise Docking Results 107
8.4 Compartmental Docking 110
8.4.1 Implementations of the ABMs 111
8.4.2 Assumptions for the ABMs 112
8.4.3 Compartmental Docking Results 114
8.5 Summary 116
9 Replication and Reproducibility (R&R) of ABMs 121
9.1 Overview 121
9.1.1 Simulation Stochasticity 122
9.1.2 Boundary Types 123
9.2 Vector Control Interventions 124
9.2.1 Larval Source Management (LSM) 125
9.2.2 Insecticide-Treated Nets (ITNs) 126
9.2.3 Population Profiles for ITNs 127
9.2.4 Coverage Schemes for ITNs 127
9.2.5 Applying LSM in Isolation 130
9.2.6 Applying ITNs in Isolation 132
9.2.7 Applying LSM and ITNs in Combination 132
9.3 Simulation Results 134
9.3.1 Simulation Stochasticity 134
9.3.2 LSM in Isolation 134
9.3.3 Impact of Boundary Types 137
9.3.4 ITNs in Isolation 138
9.3.5 LSM and ITNs in Combination 143
9.4 Replication and Reproducibility (R&R) Guidelines 147
9.5 Discussion 150
9.6 Summary 152
10 A Landscape Epidemiology Modeling Framework 155
10.1 Overview 155
10.2 GIS in Public Health 159
10.3 The Study Area and the ABM 160
10.3.1 Features of the Spatial ABM 161
10.3.2 Vector Control Intervention Scenarios 162
10.4 The Geographic Information System (GIS) 163
10.4.1 The GIS-ABM Workflow 163
10.4.2 GIS Processing of Data Layers 164
10.4.3 Feature Counts 165
10.5 Simulations and Spatial Analyses 165
10.5.1 Output Indices 166
10.5.2 Hot Spot Analysis 167
10.5.3 Kriging Analysis 167
10.6 Results 168
10.6.1 Mosquito Abundance 168
10.6.2 Oviposition Count per Aquatic Habitat 171
10.6.3 Blood Meal Count per House 174
10.7 Discussion 177
10.7.1 Stochasticity and Initial Conditions 177
10.7.2 Model Calibration and Parameterization 178
10.7.3 Emergence 178
10.7.4 Complexity 179
10.7.5 Data Resolution (Granularity) 179
10.7.6 Spatial Analyses 180
10.7.7 Habitat-based Interventions 181
10.7.8 Miscellaneous Issues 181
10.8 Conclusions 182
11 The EMOD Individual-Based Model 185
Philip A. Eckhoff and Edward A. Wenger
11.1 Overview 185
11.1.1 Motivation: Modeling of Malaria Eradication 186
11.1.2 Questions that Arise in the Context of Malaria Eradication 187
11.1.3 Spatial Heterogeneity and Metapopulation Effects 188
11.1.4 Implications for Model Structure 190
11.2 Model Structure 193
11.2.1 Human Demographics and Synthetic Population 193
11.2.2 Vector Ecology 194
11.2.3 Vector Transmission 195
11.2.4 Within-Host Disease Dynamics 197
11.2.5 Human Migration and Spatial Effects 198
11.2.6 Stochastic Ensembles 200
11.3 Results 201
11.3.1 Single-Village Simulations 201
11.3.2 Spatial Simulations: Garki District 202
11.3.3 Madagascar 203
11.4 Discussion 206
Appendix A Enzyme Kinetics Model for Vector Growth and Development 209
A.1 Overview 209
A.2 Stochastic Thermodynamic Models 210
A.3 Poikilothermic Development Models 210
A.4 The Sharpe and DeMichele Model 214
A.5 The Schoolfield et al. Model 217
A.6 Depinay et al. Model 219
A.7 Summary 221
Appendix B Flowchart for the ABM 223
B.1 Flowchart for the Agent-Based Model (ABM) 223
Appendix C Additional Files for Chapter 10 233
Appendix D A Postsimulation Analysis Module for Agent-Based Models 239
D.1 Overview 239
D.2 Simulation Output Analysis: A Review 240
D.3 The LiNK Model 243
D.4 P-SAM Architecture 245
D.5 Postsimulation Analysis and Visualization 247
D.6 P-SAM Performance 250
D.7 Conclusion 254
References 255
Index 279