Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases

Dongmei Chen, Bernard Moulin, Jianhong Wu, Dongmei Chen, Bernard Moulin, Jianhong Wu

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9781118629932
• eBook ISBN: 9781118629918
• Edition: 1
• Topic: Medicine
• Subtopic: Biostatistics

Features modern research and methodology on the spread of infectious diseases and showcases a broad range of multi-disciplinary and state-of-the-art techniques on geo-simulation, geo-visualization, remote sensing, metapopulation modeling, cloud computing, and pattern analysis Given the ongoing risk of infectious diseases worldwide, it is crucial to develop appropriate analysis methods, models, and tools to assess and predict the spread of disease and evaluate the risk. Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases features mathematical and spatial modeling approaches that integrate applications from various fields such as geo-computation and simulation, spatial analytics, mathematics, statistics, epidemiology, and health policy. In addition, the book captures the latest advances in the use of geographic information system (GIS), global positioning system (GPS), and other location-based technologies in the spatial and temporal study of infectious diseases. Highlighting the current practices and methodology via various infectious disease studies, Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases features:

• Approaches to better use infectious disease data collected from various sources for analysis and modeling purposes
• Examples of disease spreading dynamics, including West Nile virus, bird flu, Lyme disease, pandemic influenza (H1N1), and schistosomiasis
• Modern techniques such as Smartphone use in spatio-temporal usage data, cloud computing-enabled cluster detection, and communicable disease geo-simulation based on human mobility
• An overview of different mathematical, statistical, spatial modeling, and geo-simulation techniques

Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases is an excellent resource for researchers and scientists who use, manage, or analyze infectious disease data, need to learn various traditional and advanced analytical methods and modeling techniques, and become aware of different issues and challenges related to infectious disease modeling and simulation. The book is also a useful textbook and/or supplement for upper-undergraduate and graduate-level courses in bioinformatics, biostatistics, public health and policy, and epidemiology.

PART I
Overview

CHAPTER 1
Introduction to Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases

Dongmei Chen

Department of Geography, Faculty of Arts and Science, Queen's University, Kingston, ON, Canada

Bernard Moulin

Department of Computer Science and Software Engineering, Faculty of Science and Engineering, Laval University, Québec City, QC, Canada

Jianhong Wu

Department of Mathematics and Statistics, Faculty of Science, York University, Toronto, ON, Canada

1.1 Background

Infectious disease spread is a major threat to public health and economy. Based on the statistics of the World Health Organization (WHO), 25% of human death is caused by infectious diseases. The spread of an infectious disease involves characteristics of the agent such as virus and bacteria, the host, and the environment in which transmissions take place. Appropriately modeling and actually predicting the outcome of disease spread over time and across space is a critical step toward informed development of effective strategies for public health intervention (Day et al. 2006; Moghadas et al. 2008; Arino et al. 2011).

Given the ongoing risk of infectious diseases worldwide, it is important to develop appropriate analysis methods, models, and tools to assess and predict the disease spread and evaluate the disease risk. In order to ensure better understanding and to design more effective strategies for responding to existing and future disease outbreaks, questions such as the following are often asked:

1. What are the distributions of diseases across space and how do they interact with their environment? What are their origins, destinations, and spreading channels?
2. What are the potential spreading patterns of a disease across space and over time given the potential habitats of its host and its environment?
3. Which diseases will be spread around the globe successfully via global traveling and trading as well as wildlife movement (e.g., bird migration)?
4. Which parts of regions (or cities) are at the greatest risk of being exposed to a disease given urban and regional host habitats and population distributions as well as intercity and regional transportation networks?
5. Which population groups are most vulnerable to a disease?

Understanding the spatiotemporal patterns of disease spread is the key to identifying effective prevention, control, and support of infectious diseases. Recognizing the conditions under which an epidemic may occur and how a particular disease spreads is critical to designing and implementing appropriate and effective public health control measures.

Methods and tools are needed to help answer aforementioned questions involving the spatiotemporal patterns, their relevance and implications to humans and ecosystems, their impact on the vulnerability of different populations, and to develop public health policy decisions on disease prevention issues. Multidisciplinary collaboration among experts on different aspects of these diseases is important to develop and utilize these tools.

Advances in geographic information system (GIS), global positioning system (GPS), and other location-based technologies have greatly increased the availability of spatial and temporal disease and environmental data during the past 30 years. These data provide unprecedented spatial and temporal details on potential disease spreads and wildlife/human movements. While this offers many new opportunities to analyze, model, predict, and understand the spread of diseases, it also poses a great challenge on traditional disease analysis and modeling methods, which usually are not designed to handle these detailed spatial–temporal disease data. The development of different approaches to analyze and model the complicated process of disease spread that can take advantage of these spatial–temporal data and high computing performance is becoming urgent.

Through a research project jointly funded by the Canadian Network of Centers of Excellence on Geomatics for Informed Decision (GEOIDE), Mathematics of Information Technology and Complex Systems (MITACS), Public Health Agency of Canada (PHAC), and Institut national de santé publique du Québec, a network of more than 30 researchers coming from academics, government agencies, and industry in Canada, the United States, France, China, India, and other countries was established in 2008 and has since been conducting collaborative projects in selected diseases representing different modes of transmission dynamics. This network has also organized several workshops on spatial and temporal dynamics of infectious diseases.

This book represents a collection of most recent research progresses and collaboration results from this network of researchers and their collaborators. Twenty chapters contributed by fifty researchers in academic and government agencies from seven countries have been included in this book. As such, the book aims to capture the state-of-art methods and techniques for monitoring, analyzing, and modeling spatial and temporal dynamics of infectious diseases and showcasing a broad range of these methods and techniques in different infectious disease studies.

In the following, we give a brief overview of infectious diseases and the transmission mechanisms of different infectious diseases covered in this book, followed by outlining the structure and contents of this book.

1.2 Infectious Diseases, Their Transmission and Research Needs

Infectious diseases are also known as transmissible diseases or communicable diseases. The illness of infectious diseases is caused by the infection, presence, and growth of pathogenic biological agents (known as pathogens) in an individual host organism. Pathogen is the microorganism (or microbe) that causes illness. Infectious pathogens include viruses, bacteria, fungi, protozoa, multicellular parasites, and aberrant proteins known as prions. These pathogens are the cause of disease epidemics, in the sense that without the pathogen, no infectious epidemic occurs. The organism that a pathogen infects is called the host. In the human host, a pathogen causes illness by either disrupting a vital body process or stimulating the immune system to mount a defensive reaction (www.metrohealth.org). Based on the frequency of occurrence, infectious diseases can be classified as sporadic (occurs occasionally), endemic (constantly present in a population), epidemic (many cases in a region in short period), and pandemic (worldwide epidemic).

An infectious disease is termed contagious if it is easily transmitted from one person to another. The transmission mechanisms of infectious diseases can be categorized as contact transmission, vehicle transmission, and vector transmission. Contact transmission can occur by direct contact (person-to-person) between the source of the disease and a susceptible host, indirect contact through inanimate objects (such as contaminated soils), or droplet contact via mucus droplets in coughing, sneezing, laughing or talking. Vehicle transmission involves a media. Based on the media type in transmission, the infectious diseases can be categorized as airborne (diseases transmitted through the air such as influenza, anthrax, measles), foodborne (diseases transmitted through the foods such as Hepatitis A and E), and waterborne (diseases transmitted through the water such as Cholera).

A large proportion of infectious diseases are spread through vector transmission. A vector is the agent that carries and transmits an infectious pathogen from one host to another (James 2001). Vectors may be mechanical or biological. A mechanical vector picks up infectious pathogens outside of its body and transports them in a passive manner through its movement (such as housefly). The pathogen never enters or impacts the body of the vector. On the contrary, a biological vector lets the pathogen reproduce in its body. Most commonly known biological vectors are arthropods such as mosquitoes, ticks, flies, and bugs. Many biological vectors feed on blood at some or all stages of their life cycles. During the blood feeding, the pathogens enter the body of the host and cause the illness.

Understanding the disease transmission mechanism is important for infectious disease control and prevention. Many factors can influence the spreading patterns of infectious diseases. For diseases with different transmission mechanisms, factors that can impact the disease spread vary. Human mobility and social networks can greatly impact the spread of infectious diseases with contact transmission. Climate and environmental conditions can significantly impact the habitat suitability, distribution, and abundance of vectors. Climate change can influence survival and reproduction rates of vectors and pathogens within them, as well as intensity and temporal pattern of vector activity throughout the year. Human activities such as land use change, habitat disruption, pesticide use can significantly change the vector habitat and media condition, and thus impact the spread of diseases.

Quantitatively analyzing and modeling spreading of infectious diseases under different environmental and climate conditions is not new. Many methods and approaches have been developed to simulate infection process, investigate observed disease patterns, and predict future trends (see Chapter 2 in this book). Much of the past effort on disease modeling has been devoted to mathematical modeling at population level assuming various kinds of homogeneity. However, possible spatial–temporal spread and outcomes of a disease outbreak at different communiti...

Table of contents

• Cover
• Title Page
• Copyright
• Foreword: Interdisciplinary Collaborations for Informed Decisions
• Acknowledgements
• Editors
• Contributors
• Part I: Overview
• Part II: Mathematical Modeling of Infectious Diseases
• Part III: Spatial Analysis and Statistical Modeling of Infectious Diseases
• Part IV: Geosimulation and Tools for Analyzing and Simulating Spreads of Infectious Diseases
• Index
• End User License Agreement