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E-raamat: Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS

(Umm al-Qura University, Mecca, Saudi Arabia), (University of New England, Armidale, Australia)
  • Formaat: 262 pages
  • Ilmumisaeg: 01-May-2015
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482227406
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  • Formaat: 262 pages
  • Ilmumisaeg: 01-May-2015
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482227406

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This timely and groundbreaking book demonstrates how to develop models of vector borne disease risks based on different environmental and socioeconomic variables and to assess the association between these variables and their vectors in a Geographic Information System (GIS) environment. It addresses new spatial approaches and techniques based on location and environment and introduces methods to identify, determine, and analyze the trend, movement, and distribution of diseases and the vectors that transmit disease-- Master GIS Applications on Modelling and Mapping the Risks of DiseasesInfections transmitted by mosquitoes, ticks, triatomine bugs, sandflies, and black flies cause significant rates of death and disease, especially in developing countries. Why are certain places more susceptible to vector-borne diseases?Modelling Interactions Between Vector-Borne Diseases and Environment Using GIS reveals how using geographic information systems (GISs) can provide a greater understanding of how vector-borne diseases are spread and explores the use of geographical techniques in vector-borne disease monitoring, management, and control. This text provides readers with a better understanding of the vector-borne disease problem and its impact on public health.Introduces New Spatial Approaches Based on Location and EnvironmentThe book exposes readers to information on how to identify vector hotspots, determine when and where they can occur, and eliminate vector breeding sites. Utilizing simple illustrations based on real data, as well as the authors’ more than 20 years of experience in the field, this text combines key spatial analysis techniques available in modern GIS with real-world applications. It offers step-by-step instruction on developing vector-borne disease risk models at different spatial and temporal scales and helps practitioners formulate disease causation hypotheses and identify areas at risk. In addition, it addresses medical geography, GIS, spatial analysis, and modelling, and covers other factors related to the spread of vector-borne diseases.This book: Gives an overview of common vector-borne diseases, GIS-based mapping and modelling, impacts of climate change on vector distributions, and availability and importance of accurate epidemiologically relevant spatial dataDescribes modelling and simulating the prevalence of vector-borne diseases around the worldSummarizes some key spatial techniques and how they can be used to aid in the analysis of geographical and attributed dataDefines the concept of establishing and characterizing spatial data systems, including their quality, errors, references, and issues of scale, and building such a system from often quite separate, disparate sourcesShows how to develop weather-based predictive modelling, which can be used to predict the weekly trend of vector abundanceProvides a GIS case study for modelling the future potential distribution of vector-borne disease based on different climatic change scenariosModelling Interactions Between Vector-Borne Diseases and Environment Using GIS combines spatial analysis techniques available in modern GIS, together with real-world applications to provide you with a better understanding of ways to map, model, prevent, and control vector-borne diseases.

Arvustused

" really a good book. adds new information and experiences to the fields of medical geography, environmental impacts, and GIS analysis as a decision support system. Gomaa Mohamed Dawod, Survey Research Institute, Giza, Egypt

" presented in a logical, simple to understand manner. Practical examples that are conxtualised to GIS techniques were used, making the story line relevant and ready to use by decision makers." Prof Onisimo Mutanga, University of KwaZulu Natal

List of Figures xi
Foreword xv
Preface xxi
Acknowledgements xxiii
About the Authors xxv
1 Introduction 1(12)
1.1 Vector-Borne Diseases
1(1)
1.2 Mapping and Modelling Based on Geographic Information Systems
2(2)
1.3 Impacts of Climate Change on Vector Distributions
4(2)
1.4 Availability and Importance of Accurate Epidemiologically Relevant Spatial Data
6(1)
1.5 Structure of This Book
7(2)
References
9(4)
2 Modelling and Simulating the Prevalence of Vector-Borne Diseases around the World and Efforts for Combat and Control 13(40)
2.1 Introduction
13(3)
2.2 GISs in Vector-Borne Disease Modelling
16(5)
2.3 Decision Support Systems
21(2)
2.4 Spatial Analysis Capabilities of GISs
23(5)
2.5 Examples of GIS-Based Modelling of Vector-Borne Diseases
28(15)
2.5.1 Malaria
28(3)
2.5.2 Dengue Fever
31(2)
2.5.3 Rift Valley Fever
33(1)
2.5.4 Visceral Leishmaniasis (Kala-Azar)
34(1)
2.5.5 Lyme Disease
35(1)
2.5.6 Chagas Disease (American Trypanosomiasis)
36(2)
2.5.7 West Nile Virus
38(2)
2.5.8 Other Vector-Borne Diseases
40(3)
2.6 Conclusion
43(1)
References
44(9)
3 Cartographies and Maps of Vector-Borne Diseases 53(22)
3.1 Introduction
53(2)
3.2 Cartographies of Vector-Borne Diseases
55(8)
3.2.1 Title, Scale, or Distance and Direction
56(1)
3.2.2 Projection, Neat Lines, Locator, and Inset Maps
57(1)
3.2.3 Legends and Symbology
58(2)
3.2.4 Dealing with Statistical Generalization
60(3)
3.3 Mapping Vector-Borne Diseases
63(7)
3.3.1 Mapping and Modelling Malaria
65(2)
3.3.2 Mapping and Modelling Dengue Fever
67(3)
3.4 Conclusion
70(1)
References
71(4)
4 Spatial Data 75(38)
4.1 Introduction
75(3)
4.2 The Importance of Spatial Data
78(1)
4.3 Characteristics of Spatial Data, Including Topology and Topological Relationships and Their Importance
79(2)
4.3.1 Points
79(1)
4.3.2 Lines
80(1)
4.3.3 Polygons
80(1)
4.3.4 Surface
80(1)
4.4 Relationship between Entities
81(1)
4.5 Topology
82(1)
4.6 Georeferencing
83(1)
4.7 Spatial Referencing
84(2)
4.7.1 Map Projections
84(2)
4.7.2 Selecting a Map Projection
86(1)
4.8 Aggregating Geographic Data
86(2)
4.9 Data Types
88(6)
4.9.1 Raster and Vector Data Models
88(1)
4.9.2 Raster Data Model
89(2)
4.9.3 Vector Data Model
91(1)
4.9.4 So, Which Data Model to Select?
92(2)
4.10 Spatial Data Acquisition
94(1)
4.11 Data Quality Issues
95(2)
4.12 Sources of Error in GISs
97(1)
4.13 Issues of Scale
97(1)
4.14 Measurement Scales: Nominal, Ordinal, Interval, and Ratio
98(2)
4.15 Satellite Imagery as a Source of Spatial Data
100(4)
4.15.1 Remote-Sensing Applications of Vector- Borne Diseases
102(2)
4.16 Conclusion
104(2)
References
106(7)
5 Common Spatial Methods for Modelling and Analysing Spatial and Temporal Patterns and Distributions of Mosquito-Borne Diseases 113(28)
5.1 Introduction
113(2)
5.2 Pattern Analysis
115(14)
5.2.1 Average Nearest Neighbour
115(2)
5.2.2 Getis-Ord Gi*
117(4)
5.2.3 Ripley's K Function
121(4)
5.2.4 Moran's I
125(4)
5.3 Distribution Analysis
129(9)
5.3.1 Central Feature
129(3)
5.3.2 Standard Deviational Ellipse
132(2)
5.3.3 Knox Test
134(2)
5.3.4 Space Time K Function
136(1)
5.3.5 Point Buffer
136(2)
5.4 Conclusion
138(1)
References
138(3)
6 Spatial Variation Risk 141(26)
6.1 Introduction
141(1)
6.2 Density Analysis
142(7)
6.2.1 Point and Kernel
142(7)
6.3 Interpolation
149(13)
6.3.1 Inverse Distance Weighted Method
149(4)
6.3.2 Kriging
153(6)
6.3.3 Other Common Trend Surface Functions
159(2)
6.3.4 Radial Basis Function
161(1)
6.3.5 Spline
162(2)
References
164(3)
7 Modelling Associations between Mosquito-Borne Diseases and Environmental and Socioeconomic Factors 167(30)
7.1 Introduction
167(1)
7.2 Verifying Required Data
168(11)
7.2.1 Disease Data
168(4)
7.2.2 Environmental Data
172(3)
7.2.3 Socioeconomic Data
175(4)
7.3 Modelling Spatial Associations
179(13)
7.3.1 Geographically Weighted Regression and Ordinary Least Squares
179(5)
7.3.2 Linear and Multiple Regression
184(8)
References
192(5)
8 Global Climate Change and Modelling the Potential Distribution of Vector-Borne Disease 197(26)
8.1 Introduction
197(3)
8.2 Distribution of Dengue Disease and Its Vector
200(2)
8.3 CLIMEX, Climatic Models, and Data
202(4)
8.4 The Current and Potential Future Distribution
206(5)
8.5 Discussing the Potential Future Distribution
211(5)
8.6 Conclusion
216(1)
References
217(6)
9 Conclusion 223(6)
References
227(2)
Index 229
Dr. Hassan M. Khormi is an assistant professor in the Department of Geography, Umm Al-Qura University (UQU), where he teaches geographic information systems (GISs) and remote sensing. Also, Dr. Hassan is a Vice Dean of the Institute of Consulting Research and Studies. In early 2013, he accepted his position as assistant professor in the Department of Geography and as deputy director of the GIS Technology Innovation Centre for administrative affairs. He is also a consultant (part-time) at Jeddah Municipality and an adjunct lecturer in the School of Environment and Rural Science, University of New England, Australia. His main research interests are in the fields of environmental modelling and GIS applications on vector-borne diseases.

Dr. Lalit Kumar is an associate professor of spatial information technologies at the University of New England in Australia. He comes from Fiji, where he undertook his undergraduate and postgraduate studies in environmental science. Dr Kumars expertise is in the use of GISs and remote-sensing technologies for mapping and modelling the environment, particularly natural resources and agricultural systems. In addition, he has contributed to over 150 publications, with over 100 journal articles in international peer-reviewed journals. Dr. Kumar is also an associate editor for the journal ISPRS Journal of Photogrammetry and Remote Sensing and an academic editor for PLoS ONE.