Spatial Agent-Based Simulation Modeling in Public Health: An interview with co-author S.M. Niaz Arifin


  • Author: Statistics Views
  • Date: 04 May 2016

This month Wiley is proud to publish Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology by S. M. Niaz Arifin, Gregory R. Madey and Frank H. Collins.

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 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

Alison Oliver talks to co-author Professor S.M. Niaz Arifin about the book and the writing process.

thumbnail image: Spatial Agent-Based Simulation Modeling in Public Health: An interview with co-author S.M. Niaz Arifin

1. Congratulations on the upcoming publication of the book Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology which presents an overview of the complex biological systems used within a global public health setting and features a focus on malaria analysis. How did the writing process begin?

Thank you. The writing process begun after I received an invitation from Drs. Joshua G. Behr and Rafael Diaz (the editors for the Wiley Book Series in Modeling and Simulation). Dr. Behr was the Chair of the M&S Healthcare conference panel in Proceedings of the 25th European Modeling and Simulation Symposium (EMSS, September 2013, Athens, Greece), in which I presented a paper. Apparently, he was impressed by my presentation about connecting Geographic Information System (GIS) and Agent-Based Model (ABM) to model an infectious disease (malaria).

2. What were your main objectives during the writing process?

My main objectives during the writing process were to:
• Present a practical and useful introduction to the important facets of a sufficiently-complex M&S project which involved the evolution of a complex ABM
• Portray the power of ABMs as a general tool to model dynamic real-world processes often encountered in interdisciplinary, collaborative research efforts
• Demonstrate an analysis of spatial relationships between disease variables which are fundamental to public health research
• Describe the techniques and present the results of verification, validation, and replication of ABMs, which in general deal with the measurement and assessment of accuracy of M&S research
• Present a landscape epidemiology modeling framework which integrates a GIS with a spatial ABM

3. Please could you provide an example application of the use of ABMs within the book?

An ideal example application of the use of ABMs within the book can be found in Chapter 10 (Title: A Landscape Epidemiology Modeling Framework), which presents a landscape epidemiology modeling framework to integrate a geographic information system (GIS) with our spatial ABM. This is a good example to demonstrate how ABMs can be connected in various important scenarios with other software tools (e.g., a GIS) to produce meaningful results that may be of interest to a broader audience.

4. 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. There is also a companion website available. If there is one piece of information or advice that you would want your reader to take away and remember after reading your book, what would that be?

The one piece of information or advice that I would like our readers to take away and remember after reading the book is: regardless of the complexity and/or the scale of a modeling problem, modeling & simulation (M&S) techniques, especially agent-based modeling and simulation (ABMS), almost always offers an excellent modeling choice for researchers and modelers. With the ever-widening availability of computing resources, the increasing pool of computational experts, and due to its unconstrained applicability across academic discipline boundaries, this is even more applicable in today’s scientific world.

5. Who should read the book and why?

This book is intended for students, individuals, or research groups who intend to learn and use the problem-solving methodology of M&S, particularly using the ABMS techniques. It can serve as a practical resource for students with a science or engineering background, and other professionals interested in general in simulation modeling, epidemiology, public health, and bioinformatics. On the one hand, M&S researchers (including students and modelers) can benefit from the book’s description of the core conceptual model (Chapter 4) followed by the implementation details of the ABMs (Chapter 5), the extension of the non-spatial ABM into a spatial ABM (Chapter 6), and the model verification, validation, and replication issues (Chapters 7-9).

On the other hand, this book can also prove useful to a wide range of other individuals ranging from intellectuals and academics to professionals. Due to the multidisciplinary nature of the reported research that spans several academic disciplines including ABMS, bioinformatics, malaria epidemiology, spatial models, and GIS, it can have broad implications, and can be valuable to infectious disease dynamics researchers, malaria control managers (e.g., from ministries of health of malaria-endemic countries), and other public health policy makers and funding bodies.

The last two chapters (Chapters 10-11), which detail two example applications of the use of ABMs, are especially relevant for specific user groups such as M&S modelers. For example, the landscape epidemiology modeling framework presented in Chapter 10, which integrates a GIS with the spatial ABM, showcases an ideal methodological framework and an useful application of the ABMs.

6. Why is this book of particular interest now?

In today’s scientific world, computational science has become an integral aspect of almost every type of scientific research. As a tool to perform computational science, modeling & simulation (M&S) techniques, particularly agent-based models (ABMs), are being increasingly used to model complex systems. ABMs have become increasingly popular as a modeling approach in almost all branches of science and engineering. Public health and infectious disease dynamics modeling research can be termed as a signature success of ABMs.

This book is of particular interest now because of the ever-increasing importance of having complex and validated models that are capable of answering scientific questions in the right way, and augmenting the answers with meaningful analysis. For example, this book not only presents a detailed modeling endeavour, it also presents the results of verification, validation, and replication – thus ensuring increased trust and accuracy in the insights and predictions generated by the simulations. All of these are important research issues today on their own rights.

7. Were there areas of the book that you found more challenging to write, and if so, why?

We found the following areas of the book more challenging to write:

• Verification and validation (V&V) of the models: assessing the credibility of complex simulation models is often more challenging than building the models themselves; since we had a multitude of models (for various reasons, as explained in Chapter 8), we needed to ensure that all the implementations are correct realizations of the conceptual model, and that the models possessed satisfactory ranges of accuracy consistent with the intended applications.
• The landscape epidemiology modeling framework: to identify and analyze the patterns and processes of diseases across time and space, and to meaningfully integrate the models’ output with a GIS were more challenging.

8. What is it about the area of spatial agent-based simulation modeling that fascinates you?

The ability of simple agents to create complex, unexpected insights for real-world modeling problems always fascinates me. In agent-based modeling and simulation (ABMS), agents can individually assess their environment and make decisions by employing a simple set of rules. They can be adaptive, capable of evolving (allowing unanticipated behaviours to emerge), and heterogeneous. All of these simple behaviours, when combined, can discern powerful scientific insights.

9. What will be your next book-length undertaking?

Development and application of agent-based models in other research areas (e.g., to demonstrate successful merger and acquisition scenarios for financial markets).

10. You are a Research Assistant Professor in the Department of Engineering and Computer Science at the University of Notre Dame. Please could you tell us more about your educational background and what was led you to focus your career on engineering?

Before joining as a Research Assistant Professor in the Department of Engineering and Computer Science at the University of Notre Dame, I earned my Ph.D. in 2013 in the same department. My Ph.D. dissertation focused on ABMs, spatial modeling, and data warehouses. Before my Ph.D., I earned a Master of Science in Computer Science from the University of Texas at Dallas, and a Bachelor of Science in Computer Science and Engineering from Bangladesh University of Engineering & Technology (BUET).

As may be evident from my educational background, I enjoy working with scientific research problems (and trying to solve some of those). That’s the primary reason that led me to focus my career on science and engineering.

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