Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases
Samenvatting
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.
Specificaties
Inhoudsopgave
<p>Acknowledgements xi</p>
<p>Editors xiii</p>
<p>Contributors xv</p>
<p>PART I OVERVIEW</p>
<p>1 Introduction to Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases 3<br /> Dongmei Chen, Bernard Moulin, and Jianhong Wu</p>
<p>2 Modeling the Spread of Infectious Diseases: A Review 19<br /> Dongmei Chen</p>
<p>PART II MATHEMATICAL MODELING OF INFECTIOUS DISEASES</p>
<p>3 West Nile Virus: A Narrative from Bioinformatics and Mathematical Modeling Studies 45<br /> U.S.N. Murty, Amit K. Banerjee, and Jianhong Wu</p>
<p>4 West Nile Virus Risk Assessment and Forecasting Using Statistical and Dynamical Models 77<br /> Ahmed Abdelrazec, Yurong Cao, Xin Gao, Paul Proctor, Hui Zheng, and Huaiping Zhu</p>
<p>5 Using Mathematical Modeling to Integrate Disease Surveillance and Global Air Transportation Data 97<br /> Julien Arino and Kamran Khan</p>
<p>6 Malaria Models with Spatial Effects 109<br /> Daozhou Gao and Shigui Ruan</p>
<p>7 Avian Influenza Spread and Transmission Dynamics 137<br /> Lydia Bourouiba, Stephen Gourley, Rongsong Liu, John Takekawa, and Jianhong Wu</p>
<p>PART III SPATIAL ANALYSIS AND STATISTICAL MODELING OF INFECTIOUS DISEASES</p>
<p>8 Analyzing the Potential Impact of Bird Migration on the Global Spread of H5N1 Avian Influenza (2007 2011) Using Spatiotemporal Mapping Methods 163<br /> Heather Richardson and Dongmei Chen</p>
<p>9 Cloud Computing Enabled Cluster Detection Using a Flexibly Shaped Scan Statistic for Real–Time Syndromic Surveillance 177<br /> Paul Belanger and Kieran Moore</p>
<p>10 Mapping the Distribution of Malaria: Current Approaches and Future Directions 189<br /> Leah R. Johnson, Kevin D. Lafferty, Amy McNally, Erin Mordecai, Krijn P. Paaijmans, Samraat Pawar, and Sadie J. Ryan</p>
<p>11 Statistical Modeling of Spatiotemporal Infectious Disease Transmission 211<br /> Rob Deardon, Xuan Fang, and Grace P.S. Kwong</p>
<p>12 Spatiotemporal Dynamics of Schistosomiasis in China: Bayesian–Based Geostatistical Analysis 233<br /> Zhi–Jie Zhang</p>
<p>13 Spatial Analysis and Statistical Modeling of 2009 H1N1 Pandemic in the Greater Toronto Area 247<br /> Frank Wen, Dongmei Chen, and Anna Majury</p>
<p>14 West Nile Virus Mosquito Abundance Modeling Using Nonstationary Spatiotemporal Geostatistics 263<br /> Eun–Hye Yoo, Dongmei Chen, and Curtis Russel</p>
<p>15 Spatial Pattern Analysis of Multivariate Disease Data 283<br /> Cindy X. Feng and Charmaine B. Dean</p>
<p>PART IV GEOSIMULATION AND TOOLS FOR ANALYZING AND SIMULATING SPREADS OF INFECTIOUS DISEASES</p>
<p>16 The ZoonosisMAGS Project (Part 1): Population–Based Geosimulation of Zoonoses in an Informed Virtual Geographic Environment 299<br /> Bernard Moulin, Mondher Bouden, and Daniel Navarro</p>
<p>17 ZoonosisMAGS Project (Part 2): Complementarity of a Rapid–Prototyping Tool and of a Full–Scale Geosimulator for Population–Based Geosimulation of Zoonoses 341<br /> Bernard Moulin, Daniel Navarro, Dominic Marcotte, Said Sedrati, and Mondher Bouden</p>
<p>18 Web Mapping and Behavior Pattern Extraction Tools to Assess Lyme Disease Risk for Humans in Peri–urban Forests 371<br /> Hedi Haddad, Bernard Moulin, Franck Manirakiza, Christelle M´eha, Vincent Godard, and Samuel Mermet</p>
<p>19 An Integrated Approach for Communicable Disease Geosimulation Based on Epidemiological, Human Mobility and Public Intervention Models 403<br /> Hedi Haddad, Bernard Moulin, and Marius Thériault</p>
<p>20 Smartphone Trajectories as Data Sources for Agent–based Infection–spread Modeling 443<br /> Marcia R. Friesen and Robert D. McLeod</p>
<p>Index 473</p>