Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases -- Contents -- Foreword: Interdisciplinary Collaborations for Informed Decisions -- Acknowledgements -- Editors -- Contributors -- PART I Overview -- 1 Introduction to Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases -- 1.1 Background -- 1.2 Infectious Diseases, Their Transmission and Research Needs -- 1.3 Diseases Covered in This Book and Their Transmission Mechanism -- 1.3.1 West Nile Virus -- 1.3.2 Lyme Disease -- 1.3.3 Avian and Human Influenza -- 1.3.4 Schistosomiasis -- 1.3.5 Malaria -- 1.3.6 Sexually Transmitted Diseases -- 1.4 The Organization and Outline of This Book -- 1.4.1 Mathematical Modeling of Infectious Diseases -- 1.4.2 Spatial Analysis and Statistical Modeling of Infectious Diseases -- 1.4.3 Geosimulation and Tools for Analyzing and Simulating Spreads of Infectious Diseases -- 1.5 Conclusion -- References -- 2 Modeling the Spread of Infectious Diseases: A Review -- 2.1 Introduction -- 2.2 Mathematical Modelling -- 2.2.1 Classical Mathematical Models -- 2.2.2 Spatiotemporal Mathematical Disease Modeling -- 2.3 Statistical Modeling -- 2.4 Gravity Models -- 2.5 Network-Based Models -- 2.6 Computational Simulation Approaches -- 2.6.1 Cellular Automation Simulation -- 2.6.2 Field Simulation Modeling -- 2.6.3 Individual or Agent-Based Modeling -- 2.7 Discussions and Conclusions -- Acknowledgments -- References -- PART II Mathematical Modeling of Infectious Diseases -- 3 West Nile Virus: A Narrative from Bioinformatics and Mathematical Modeling Studies -- 3.1 Introduction -- References -- 4 West Nile Virus Risk Assessment and Forecasting Using Statistical and Dynamical Models -- 4.1 Introduction -- 4.2 Statistical Model for Mosquito Abundance of WNV -- 4.3 Risk Assessment of WNV Using the Dynamical Model -- 4.3.1 DMIR Model.

4.3.2 The Initial Conditions in DMIR Index -- 4.3.3 R0 and DMIR -- 4.4 Forecasting WNV Risk in Peel Region, Ontario, Using Real Data -- 4.4.1 Mosquito Abundance -- 4.4.2 WNV Risk Forecasting -- 4.4.3 Numerical Simulations -- 4.5 Conclusions -- Acknowledgments -- References -- 5 Using Mathematical Modeling to Integrate Disease Surveillance and Global Air Transportation Data -- 5.1 Introduction -- 5.2 The Network -- 5.3 Airport Catchment Areas -- 5.4 Modeling -- 5.4.1 The Model in Airport Catchment Areas -- 5.4.2 Movement Rates -- 5.4.3 General Model of Infection-Transport -- 5.4.4 Initial Conditions -- 5.4.5 Parameter Estimation -- 5.5 Numerical Simulations -- 5.6 Conclusions -- References -- 6 Malaria Models with Spatial Effects -- 6.1 Introduction -- 6.2 Malaria Models with Constant Infective Immigrants -- 6.3 Malaria Models with Discrete Diffusion -- 6.3.1 Multi-patch Models without Vital Dynamics of Humans -- 6.3.2 Multi-patch Models with Vital Dynamics of Humans -- 6.3.3 Multi-patch and Multi-strain Malaria Models -- 6.4 Malaria Models with Continuous Diffusion -- 6.5 Discussion -- Acknowledgments -- References -- 7 Avian Influenza Spread and Transmission Dynamics -- 7.1 Introduction -- 7.1.1 Organization and Acknowledgment -- 7.2 Avian Influenza: Issues for Modelling -- 7.2.1 Spatial Dynamics of Migratory Birds -- 7.2.2 Impact of Disease Bird Ecology -- 7.2.3 Global Spread and Disease Epidemiology -- 7.2.4 Novel Mathematics: Finite Dimension Reduction -- 7.3 HPAI Outbreak Mitigated by Seasonal LPAI -- 7.4 Local Dynamics and Mitigation Potential -- 7.4.1 Local Control -- 7.4.2 Impact of the Local Diffusion and Commercial Poultry Trading -- 7.5 Conclusion -- References -- PART III Spatial Analysis and Statistical Modeling of Infectious Diseases.

8 Analyzing the Potential Impact of Bird Migration on the Global Spread of H5N1 Avian Influenza (2007-2011) Using Spatiotemporal Mapping Methods -- 8.1 Introduction -- 8.2 Methodology -- 8.2.1 Data -- 8.2.2 Methods of Analysis -- 8.3 Results and Discussion -- 8.4 Conclusion -- Acknowledgments -- References -- 9 Cloud Computing-Enabled Cluster Detection Using a Flexibly Shaped Scan Statistic for Real-Time Syndromic Surveillance -- 9.1 Introduction -- 9.2 Spatial Scan Statistics -- 9.3 Study Region and Data -- 9.4 Computational Challenge -- 9.5 Discussion -- Acknowledgments -- References -- 10 Mapping the Distribution of Malaria: Current Approaches and Future Directions -- 10.1 Introduction -- 10.2 Mapping and Spatial Models -- 10.2.1 Types of Data and Covariates Used for Spatial Analyses of Malaria -- 10.3 Modern Mapping Approaches and Methods -- 10.3.1 Spatially Explicit Models -- 10.3.2 Implicit Models -- 10.3.3 Hybrid Approaches -- 10.4 Future Directions and Conclusions -- References -- 11 Statistical Modeling of Spatiotemporal Infectious Disease Transmission -- 11.1 Introduction -- 11.2 Infectious Disease Transmission Model -- 11.2.1 SIR Framework and Associated Notation -- 11.2.2 The General Model -- 11.2.3 Adding Spatial Structure and Other Explanatory Variables -- 11.2.4 Example Models -- 11.3 Statistical and Computational Framework -- 11.3.1 ILM Likelihood Function -- 11.3.2 Bayesian Inference -- 11.3.3 Markov Chain Monte Carlo -- 11.3.4 MCMC Analysis for Geometric Spatial ILMs -- 11.3.5 MCMC for Geometric ILM with Discrete Spatial Parameter -- 11.3.6 Some Simulation Results -- 11.4 Discussion -- Acknowledgments -- References -- 12 Spatiotemporal Dynamics of Schistosomiasis in China: Bayesian-Based Geostatistical Analysis -- 12.1 Introduction -- 12.2 Materials and Methods -- 12.2.1 Study Area -- 12.2.2 Parasitological Data.

12.2.3 Digitized Maps of the Studied Region and Lakes -- 12.2.4 Location of Snail Habitats -- 12.2.5 Digital Elevation Model -- 12.2.6 Remote Sensing Images -- 12.2.7 Statistical Analysis -- 12.2.8 Model Validation -- 12.3 Results -- 12.3.1 S. Japonicum Surveyed Villages -- 12.3.2 Covariates and Their Relationships with Schistosomiasis -- 12.3.3 Results of Model Fitting -- 12.3.4 Model Evaluation -- 12.3.5 Spatiotemporal Pattern of S. Japonicum Risk -- 12.4 Discussion -- Acknowledgments -- References -- 13 Spatial Analysis and Statistical Modeling of 2009 H1N1 Pandemic in the Greater Toronto Area -- 13.1 Introduction -- 13.2 Study Area And Data -- 13.3 Analysis Methods -- 13.3.1 Initial Data Exploratory Analysis -- 13.3.2 Modeling the H1N1 Infection Risk -- 13.4 The Implementation of the Glmm and Icar -- 13.5 Results -- 13.5.1 Initial Exploratory Data Analysis -- 13.5.2 Model Fitting Results -- 13.6 Discussions And Conclusion -- References -- 14 West Nile Virus Mosquito Abundance Modeling Using Nonstationary Spatiotemporal Geostatistics -- 14.1 Introduction -- 14.2 Methods -- 14.2.1 Spatial-Temporal Process Modeling -- 14.2.2 Moving-Cylinder Spatiotemporal Kriging -- 14.3 Data Analysis and Results -- 14.3.1 Mosquito Surveillance Data -- 14.3.2 Global Space-Time Poisson Regression -- 14.3.3 Local Spatiotemporal Model Calibration -- 14.3.4 Local Space-Time Poisson Regression -- 14.4 Summary and Conclusions -- References -- 15 Spatial Pattern Analysis of Multivariate Disease Data -- 15.1 Introduction -- 15.2 The CBR Data -- 15.3 Models and Methods -- 15.3.1 Zero-Inflated Common Spatial Factor Model -- 15.3.2 Zero-Inflated Spatial Bivariate Model -- 15.3.3 Posterior Computation and Model Selection -- 15.4 Analysis of The CBR Data -- 15.5 Discussion -- Acknowledgments -- References.

PART IV Geosimulation and Tools for Analyzing and Simulating Spreads of Infectious Diseases -- 16 The ZoonosisMAGS Project (Part 1): Population-Based Geosimulation of Zoonoses in an Informed Virtual Geographic Environment -- 16.1 Introduction -- 16.2 Spatially Explicit Models for Epidemiology -- 16.3 Simulation Approaches of Disease Propagation -- 16.4 The Zoonosismags Population-Based Geosimulation Approach -- 16.5 The Informed Virtual Geographic Environment -- 16.6 Spatialized Population-Based Approach -- 16.7 Modeling and Simulating Mobility -- 16.8 Simulation of The Establishment of Tick Populations -- 16.9 Conclusion -- Acknowledgments -- References -- 17 ZoonosisMAGS Project (Part 2): Complementarity of a Rapid-Prototyping Tool and of a Full-Scale Geosimulator for Population-Based Geosimulation of Zoonoses -- 17.1 Introduction -- 17.2 The Zoonosismags Project and Our Double Software Development Strategy -- 17.3 The MATLAB Simulation Prototyping Tool -- 17.3.1 Conceptual Model -- 17.3.2 Architecture and Simulation Logic of the MATLAB Geosimulator -- 17.3.3 Scenario Specification Interface -- 17.4 Experiments Carried out with Our MATLAB Simulator -- 17.4.1 Assessment of a System Dynamics Simulation -- 17.4.2 Sensitivity Analysis -- 17.4.3 Generation of Test Data for the C++ Geosimulator -- 17.4.4 Conclusion -- 17.5 The C++ Full-Scale Geosimulator -- 17.6 Current Status Of The Implementation and Future Work -- 17.7 Conclusion -- Acknowledgments -- References -- 18 Web Mapping and Behavior Pattern Extraction Tools to Assess Lyme Disease Risk for Humans in Peri-urban Forests -- 18.1 Assessment of Human Risk Exposure to Lyme Disease -- 18.1.1 Lyme Disease Dynamics and Human Exposure -- 18.1.2 Assessment of Human Risk Exposure -- 18.2 The SÉNart-Mags Project -- 18.3 Visitors Data Collection -- 18.3.1 Paper Questionnaires.

18.3.2 Web Mapping-Based Questionnaire.