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

• Publisher: Wiley
• Year: 2016
• ISBN:
  9781118964361
• Edition: 1
• Topic: Medicine
• Subtopic: Epidemiology

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.

CHAPTER 1
INTRODUCTION

1.1 OVERVIEW

In recent years, modeling and simulation (M&S) paradigms are being increasingly used as an efficient tool to model complex systems, which often exhibit complex properties such as emergence, learning, adaptation, heterogeneity, and dynamic interactions. With the ever-widening availability of computing resources, the increasing pool of human computational experts, and due to its unconstrained applicability across academic discipline boundaries, the importance of M&S continues to grow at a remarkable rate. M&S draws from academic disciplines such as computer science, mathematics, operational research, engineering, statistics, and physics, and covers a broad set of application areas such as biology, public health, commerce, defense, logistics, manufacturing, supply chains, and transportation [525]. Typical roles of M&S studies include forecasting, sensitivity analysis, comparison of control policy options, education and training, engineering design, performance evaluation, prototyping and concept evaluation, risk and safety assessment, and uncertainty reduction in decision-making [67].

Agent-based modeling and simulation (ABMS) is an M&S technique for simulating the actions and interactions of autonomous agents with a view to assessing their effects on the simulated system as a whole. Having its roots in the investigation of complex systems, complex adaptive systems, and artificial life, ABMS has evolved as a natural response to meet the needs of complex systems modeling [525]. The suite of models developed using ABMS, known as agent-based models (ABMs), have applications in diverse real-world problems and have become increasingly popular as a modeling approach in social sciences and public health research problems.¹ To this end, the advances in epidemics and infectious disease dynamics research made possible through the use of ABMs can be termed as one of its signature successes [321].

This book primarily concerns spatial agent-based simulation modeling in public health. In particular, it presents the design, implementation, and applications of spatialABMs for malaria, which is one of the largest causes of global human mortality and morbidity. The ABMs simulate the life cycle and the population dynamics of the malaria-transmitting mosquito vector Anopheles gambiae (An. gambiae for short),² from a biological core model.³

Figure 1.1 depicts the major components of the book. Logical connections between the components and the chapters are indicated, which may serve as visual cues for the readers. Chapters 2 and 3 present some general background of malaria and ABMs, and discuss the applicability of ABMs in malaria epidemiology research, which, in turn, broadly falls under the realm of computational biology. Chapters 4–6 describe the biological core model and the ABMs (including the spatial ABMs), and form the core of the book. Chapters 7–9 discuss the verification, validation, and replication issues of the ABMs. Chapter 10 presents a landscape epidemiology modeling framework that integrates the simulation outputs from the spatial ABMs with a geographic information system (GIS). Finally, Chapter 11 presents another spatial ABM – the epidemiological modeling EMOD individual-based model (IBM).^4,5 Note that some chapters may overlap into multiple components (see Fig. 1.1). All components of the book share the global/public health implications.

Figure 1.1 Book components. Logical connections between the major components and the chapters are indicated. This book primarily concerns modeling & simulation (M&S), specifically agent-based modeling (ABM), in public health (malaria epidemiology). Chapters 2 and 3 present some general background of malaria and agent-based models (ABMs) and discuss the applicability of ABMs in malaria epidemiology research, which, in turn, broadly falls under the realm of computational biology. Chapters 4–6 describe the biological core model, the agent-based models (ABMs), and the spatial ABMs, and form the core of the book. Chapters 7–9 discuss the verification, validation, and replication issues of the ABMs. Chapter 10 presents a landscape epidemiology modeling framework, and Chapter 11 presents the EMOD individual-based model. Note that some chapters may overlap into multiple components. All components of the book share the global/public health implications.

The remainder of this chapter provides a brief introduction to malaria and ABMs. We conclude this chapter by listing our specific contributions, and by providing a roadmap for the remainder of this book.

1.2 MALARIA

Malaria is one of the oldest and deadliest infectious diseases in humans. According to the latest estimates (released in December 2014), the World Health Organization (WHO) reported about 198 million cases of malaria in 2013 and an estimated 584,000 deaths [568]. Half of the world's population (about 3.3 billion) is at risk of malaria [568]. The population of sub-Saharan Africa, which accounts for

of the globally estimated annual deaths, experiences the highest burden of the disease, with a child dying every minute [568]. As highland malaria re-emerged in several African countries in the recent decades [443], the control of malaria represents one of the greatest global public health challenges of the twenty-first century.

Malaria is a complex disease. Being global in nature, it imposes a staggering burden on the public health. A variety of geographical, environmental, vector, host, and parasite factors determine its local and global characteristics. Different population groups in malaria-infected areas face different risks with regard to the disease. These groups range from pregnant women, infants, and young children to diversified groups such as travelers and migrants.

Caused by protozoan parasites of the genus Plasmodium, human malaria is transmitted only by female mosquitoes of the genus Anopheles. The group of Anopheles species known as the Anopheles gambiae complex, which refers to eight very closely related and morphologically indistinguishable species, includes the two most important malaria vectors in sub-Saharan Africa and the most efficient malaria vectors in the world: An. gambiae and Anopheles coluzzii.

1.3 AGENT-BASED MODELING OF MALARIA

An agent-based model (ABM) is typically an object-oriented, discrete-event, rule-based, stochastic, and often spatially explicit model for dynamic computational M&S [9, 72, 256].⁶ ABMs are increasingly used to represent and investigate multiscale complex systems and have applications in diverse real-world problems.

An ABM consists of a collection of autonomous decision-making entities called agents [72]. By employing a set of rules, agents can individually assess the environment and may make decisions. Appropriate behaviors for the system maythen be executed by the agents. ABMs possess inherent abilities to capture important modeling aspects such as space, geometry, and structure, which collectively facilitate the ability of modelers to express, represent, and test their hypotheses.

ABMs of malaria can play important roles in quantifying the effects of malaria-control interventions and in answering other interesting research questions. For example, ABMs can assist malaria control managers and policy makers in selecting appropriate combinations of interventions to interrupt transmission and in setting response timelines and expectations of impact. They can also be usefu...

Table of contents

• COVER
• WILEY SERIES IN MODELING AND SIMULATION
• TITLE PAGE
• COPYRIGHT
• DEDICATION
• TABLE OF CONTENTS
• LIST OF CONTRIBUTORS
• LIST OF FIGURES
• LIST OF TABLES
• PREFACE
• ACKNOWLEDGMENTS
• LIST OF ABBREVIATIONS
• CHAPTER 1: INTRODUCTION
• CHAPTER 2: MALARIA: A BRIEF HISTORY
• CHAPTER 3: AGENT-BASED MODELING AND MALARIA
• CHAPTER 4: THE BIOLOGICAL CORE MODEL
• CHAPTER 5: THE AGENT-BASED MODEL (ABM)
• CHAPTER 6: THE SPATIAL ABM
• CHAPTER 7: VERIFICATION, VALIDATION, REPLICATION, AND REPRODUCIBILITY
• CHAPTER 8: VERIFICATION AND VALIDATION (V&V) OF ABMs
• CHAPTER 9: REPLICATION AND REPRODUCIBILITY (R&R) OF ABMs
• CHAPTER 10: A LANDSCAPE EPIDEMIOLOGY MODELING FRAMEWORK
• CHAPTER 11: THE EMOD INDIVIDUAL-BASED MODEL
• APPENDIX A: ENZYME KINETICS MODEL FOR VECTOR GROWTH AND DEVELOPMENT
• APPENDIX B: FLOWCHART FOR THE ABM
• APPENDIX C: ADDITIONAL FILES FOR CHAPTER 10
• APPENDIX D: A POSTSIMULATION ANALYSIS MODULE FOR AGENT-BASED MODELS
• REFERENCES
• INDEX
• END USER LICENSE AGREEMENT