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Advisory Editors • Public and Population Health

Peter S. Hovmand, Washington University in St. Louis

Bruce Y. Lee, University of Pittsburgh

 

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Rafael Diaz, Old Dominion University

 

Homeland Security, Emergency Management, and Risk Analysis

 

Forthcoming Titles

Zedda • Risk and Stability of Banking Systems

 

Interoperability, Composability, and Formalism

 

Operations Research, Logistics, Supply Chains, and Transportation

 

Public and Population Health

 

Arifin, Madey, and Collins • Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology

 

Forthcoming Titles

 

Hovmand • Modeling Social Determinants of Health

Kim and Hammon • Modeling and Simulation for Social Epidemiology and Public Health

 

Training and Education

 

Combs, Sokolowski, and Banks • The Digital Patient: Advancing Healthcare, Research, and Education

 

Forthcoming Titles

 

Tolk and Ören • The Profession of Modeling and Simulation

SPATIAL AGENT-BASED SIMULATION MODELING IN PUBLIC HEALTH

Design, Implementation, and Applications for Malaria Epidemiology

 

S. M. NIAZ ARIFIN

Department of Computer Science and Engineering
University of Notre Dame
IN, USA

 

GREGORY R. MADEY

Department of Computer Science and Engineering
University of Notre Dame
IN, USA

 

FRANK H. COLLINS

Department of Biological Sciences
University of Notre Dame
IN, USA

 

 

 

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Library of Congress Cataloging-in-Publication Data:

Arifin, S.M. Niaz, author.

Spatial agent-based simulation modeling in public health : design, implementation, and applications for malaria epidemiology / S.M. Niaz Arifin, Gregory R. Madey, Frank H. Collins.

p. ; cm.

Includes bibliographical references and index.

ISBN 978-1-118-96435-4 (hardback)

I. Madey, Gregory Richard, author. II. Collins, Frank H., author. III. Title.

[DNLM: 1. Malaria–epidemiology. 2. Computer Simulation. 3. Geographic Information Systems. 4. Models, Theoretical. 5. Spatial Analysis. WC 755.1]

RA644.M2

614.5′32090285–dc23

2015033121

To my parents:

Engineer S. M. Golam Mostofa

B.Sc. Engg. (Civil), FIE (B), PGD (CS)

My Father and Guide

Professor Parvin Akhter Jahan

M.A. (Economics), B.A. (Honors)

My Mother and Best Friend

and my wife:

Rumana Reaz Arifin

B.S., M.S.

My Soulmate

and my sister:

Mafruhatul Jannat

Ph.D., M.S., B.S.

We Grew up Together

- S. M. Niaz Arifin

LIST OF CONTRIBUTORS

  1. Philip A. Eckhoff Research Scientist, Principal Investigator, Institute for Disease Modeling (IDM), Intellectual Ventures Management, LLC (IV), Bellevue, WA, USA
  2. Edward A. Wenger, Sr. Research Manager, Institute for Disease Modeling (IDM), Intellectual Ventures Management, LLC (IV), Bellevue, WA, USA

LIST OF FIGURES

  1. Figure 1.1 Book components.
  2. Figure 2.1 Life cycle of the malaria parasite.
  3. Figure 4.1 Life cycle of mosquitoes in the models.
  4. Figure 4.2 The egg hatching time distribution used in the core model.
  5. Figure 5.1 A simplified class diagram of the model architecture in the ABM.
  6. Figure 5.2 A simplified class diagram for the agents in the ABM.
  7. Figure 5.3 A simplified class diagram for the environments in the ABM.
  8. Figure 5.4 A simplified class diagram for the agentlists in the ABM.
  9. Figure 5.5 An event-action-list (EAL) diagram for the ABM.
  10. Figure 5.6 The ordering of the key processing steps performed in a single time step of a simulation run.
  11. Figure 5.7 Dependency relationships in processing steps ordering can be viewed as a directed acyclic graph (DAG).
  12. Figure 6.1 Examples of three types of landscapes: (a) regular, (b) random, and (c) hybrid.
  13. Figure 6.2 Screenshot of an early version of the landscape generator tool, AnophGUI.
  14. Figure 6.3 Screenshot of the latest version of the landscape generator tool, VectorLand.
  15. Figure 6.4 Examples of landscapes with different clustering schemes of resources.
  16. Figure 6.5 Controlling the clusters along a specific axis.
  17. Figure 6.6 Foraging event for mosquito agents.
  18. Figure 6.7 Flight heuristics for mosquito agents.
  19. Figure 6.8 Model verification results.
  20. Figure 6.9 Results of using different landscape patterns.
  21. Figure 6.10 Results for resource size variation with 1 and 16 aquatic habitats.
  22. Figure 6.11 Results for resource size variation with 49 and 100 aquatic habitats.
  23. Figure 6.12 Results for resource density variation.
  24. Figure 6.13 Results for system capacity variation.
  25. Figure 6.14 Sample 30 × 30 landscapes.
  26. Figure 6.15 Results for resource density below the critical level.
  27. Figure 6.16 Results for resource density above the critical level.
  28. Figure 8.1 The phase-wise docking workflow.
  29. Figure 8.2 Phase-wise docking results for Phase 1.
  30. Figure 8.3 Phase-wise docking results for Phase 3.
  31. Figure 8.4 The compartmental docking workflow.
  32. Figure 8.5 Simplified life cycle of mosquitoes for compartmental docking.
  33. Figure 8.6 Compartmental docking results for Phase 1.
  34. Figure 8.7 Compartmental docking results for Phase 2.
  35. Figure 8.8 Compartmental docking results for Phase 3.
  36. Figure 9.1 Coverage schemes for ITNs.
  37. Figure 9.2 Landscapes for Applying LSM in Isolation.
  38. Figure 9.3 Landscape for Applying ITNs in Isolation.
  39. Figure 9.4 Sample landscapes for applying LSM and ITNs in combination.
  40. Figure 9.5 Sufficient number of replicated simulation runs can smooth out the simulation stochasticity effects.
  41. Figure 9.6 The figure depicts the full 1-year results of applying LSM in isolation with absorbing boundaries as we replicate the results of GN-LSM [222].
  42. Figure 9.7 The figure depicts the full 1-year results of applying LSM in isolation with nonabsorbing boundaries as we replicate the results of GN-LSM [222].
  43. Figure 9.8 The figure depicts the full 1-year results of applying ITNs in isolation with household-level partial coverage and single chance for host-seeking as we replicate the results of GN-ITNs [221].
  44. Figure 9.9 The figure depicts the full 1-year results of applying ITNs in isolation with household-level complete coverage as we replicate the results of GN-ITNs [221].
  45. Figure 9.10 The figure depicts the full 1-year results of applying ITNs in isolation with household-level partial coverage and multiple chances for host-seeking as we replicate the results of GN-ITNs [221].
  46. Figure 9.11 Percent reductions in mosquito abundance by ITNs, applied in isolation, comparing household-level partial coverage (with multiple chances for host-seeking) and complete coverage.
  47. Figure 9.12 Percent reductions in mosquito abundance by ITNs, applied in isolation, comparing household-level partial coverage (with multiple chances for host-seeking) and complete coverage.
  48. Figure 9.13 Percent reductions in mosquito abundance as a function of LSM coverage and ITNs coverage when LSM and ITNs are applied in combination.
  49. Figure 10.1 The study area.
  50. Figure 10.2 Selected sets of GIS features for Kenya.
  51. Figure 10.3 Maps for the mosquito abundances index.
  52. Figure 10.4 Kriged maps for the mosquito abundances index.
  53. Figure 10.5 Maps for the oviposition count per aquatic habitat index.
  54. Figure 10.6 Kriged maps for the oviposition count per aquatic habitat index.
  55. Figure 10.7 Maps for the blood meal count per house index.
  56. Figure 10.8 Kriged maps for all scenarios for the blood meal count per house index.
  57. Figure 11.1 The EMOD model of the mosquito feeding cycle with outcomes.
  58. Figure 11.2 Simulation of Namawala, Tanzania, as described in [160], shows changes in EIR due to seasonal variations at baseline (a), with IRS (b), and with IRS and a transmission-blocking vaccine (c).
  59. Figure 11.3 Outcomes of different intervention combinations in the Garki District [163].
  60. Figure 11.4 Modeled distribution of detected prevalence in Madagascar.
  61. Figure 11.5 Modeled vector population density distributions in Madagascar.
  62. Figure 11.6 (a) Separatrix plots of the modeled probability of successful Eradication in Madagascar, showing the division of the high probability of success region separated from the low probability of success region.
  63. Figure A.1 Energy states of the kinetic model (redrawn from [488]).
  64. Figure C.1 Clipped eater sources for Kenya.
  65. Figure C.2 Clipped village projections for Kenya.
  66. Figure C.3 Polygon creation process.
  67. Figure C.4 Clipped habitats within the selected polygon.
  68. Figure C.5 Data conversion to raster format, Part 1.
  69. Figure C.6 Data conversion to raster format, Part 2.
  70. Figure D.1 The P-SAM architecture.
  71. Figure D.2 P-SAM Infection Statistics tab.
  72. Figure D.3 P-SAM Roaming Infection Statistics tab.
  73. Figure D.4 P-SAM birth and death statistics tab.
  74. Figure D.5 Example of a pathogen transmission graph.
  75. Figure D.6 P-SAM Summary Statistics tab.
  76. Figure D.7 P-SAM performance.

LIST OF TABLES

  1. Table 3.1 Applications of Agent-Based Models (ABMs)
  2. Table 3.2 Malaria Models: A Comparison of Features
  3. Table 4.1 Summary of Updated Features in the Core Model
  4. Table 4.2 Symbols and parameters used in the core model and the ABMs. Parameters are listed in order of appearance in the text
  5. Table 4.3 Larval development parameters for An. gambiae
  6. Table 7.1 Methodologies and Techniques Commonly Used for V&V in M&S Research
  7. Table 8.1 V&V Techniques Used for the ABMs
  8. Table 8.2 Simplified Stage Transition Times for Phase-Wise Docking
  9. Table 8.3 Compartmental Docking Issues in Phase 1
  10. Table 8.4 Compartmental Docking Issues in Phases 2–3
  11. Table 9.1 Population Profiles for Varying Levels of ITNs Coverage
  12. Table 9.2 Parameter Space for ITNs
  13. Table 9.3 Parameter Space for LSM and ITNs
  14. Table 9.4 Percent Reductions in Abundance with LSM
  15. Table 10.1 Vector Control Intervention Scenarios
  16. Table 10.2 GIS Feature Types and Counts for the ABM
  17. Table 11.1 Summary of Issues for Eradication Modeling
  18. Table 11.2 Summary of Features Desired for Eradication Modeling
  19. Table A.1 Enzyme, Reaction, and Rate Constant
  20. Table A.2 Entropy and Enthalpy of Activation
  21. Table D.1 Perl Extension Modules Used