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Dongmei Chen, PhD, is Associate Professor in the Department of Geography and Director of the Laboratory for Geographic Information and Spatial Analysis at Queen’s University, Canada.

Bernard Moulin, PhD, is Professor in the Department of Computer Science and Software Engineering at Laval University, Canada.

Jianhong Wu, PhD, is Canada Research Chair and University Distinguished Research Professor in the Department of Mathematics and Statistics and Director of the Center for Disease Modeling at York University, Canada.

Cover Image: Courtesy of Dongmei Chen

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.

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 ANALYZING AND MODELING SPATIAL AND TEMPORAL DYNAMICS OF INFECTIOUS DISEASESEDITED BY DONGMEI CHEN • BERNARD MOULIN • JIANHONG WU

ANALYZIN

G AND M

ODELING SPATIAL AN

D TEM

PORAL DYNAM

ICS OF INFECTIOUS DISEASES

CHEN MOULIN

WU

1.18”

Analyzing and Modeling Spatialand Temporal Dynamics ofInfectious Diseases

Analyzing and Modeling Spatialand Temporal Dynamics ofInfectious Diseases

Edited by

DONGMEI CHEN

Department of GeographyQueen’s UniversityKingston, Canada

BERNARD MOULIN

Department of Computer Science and Software EngineeringLaval UniversityQuebec, Canada

JIANHONG WU

Department of Mathematics and StatisticsYork UniversityToronto, Canada

Copyright © 2015 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form orby any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except aspermitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the priorwritten permission of the Publisher, or authorization through payment of the appropriate per-copy fee tothe Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400,fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permissionshould be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken,NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.

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Library of Congress Cataloging-in-Publication Data:

Analyzing and modeling spatial and temporal dynamics of infectious diseases / [edited by] DongmeiChen, Bernard Moulin, Jianhong Wu.

p. ; cm.Includes bibliographical references and index.ISBN 978-1-118-62993-2 (cloth)

I. Chen, Dongmei, 1969- editor. II. Moulin, Bernard, 1954- editor. III. Wu, Jianhong, 1964- editor.[DNLM: 1. Communicable Diseases–epidemiology. 2. Spatio-Temporal Analysis. 3. Computer

Simulation. 4. Disease Transmission, Infectious–statistics & numerical data. 5. Models, Statistical.WA 950]

RC111616.9–dc23

2014011438

Printed in the United States of America

10 9 8 7 6 5 4 3 2 1

Contents

Foreword ixNicholas Chrisman

Acknowledgements xi

Editors xiii

Contributors xv

PART I OVERVIEW

1 Introduction to Analyzing and Modeling Spatial and TemporalDynamics of Infectious Diseases 3

Dongmei Chen, Bernard Moulin, and Jianhong Wu

2 Modeling the Spread of Infectious Diseases: A Review 19

Dongmei Chen

PART II MATHEMATICAL MODELING OF INFECTIOUSDISEASES

3 West Nile Virus: A Narrative from Bioinformatics andMathematical Modeling Studies 45

U.S.N. Murty, Amit K. Banerjee, and Jianhong Wu

4 West Nile Virus Risk Assessment and Forecasting Using Statisticaland Dynamical Models 77

Ahmed Abdelrazec, Yurong Cao, Xin Gao, Paul Proctor, Hui Zheng, andHuaiping Zhu

v

vi CONTENTS

5 Using Mathematical Modeling to Integrate Disease Surveillance andGlobal Air Transportation Data 97

Julien Arino and Kamran Khan

6 Malaria Models with Spatial Effects 109

Daozhou Gao and Shigui Ruan

7 Avian Influenza Spread and Transmission Dynamics 137

Lydia Bourouiba, Stephen Gourley, Rongsong Liu, John Takekawa, andJianhong Wu

PART III SPATIAL ANALYSIS AND STATISTICAL MODELINGOF INFECTIOUS DISEASES

8 Analyzing the Potential Impact of Bird Migration on the GlobalSpread of H5N1 Avian Influenza (2007–2011) Using SpatiotemporalMapping Methods 163

Heather Richardson and Dongmei Chen

9 Cloud Computing–Enabled Cluster Detection Using a FlexiblyShaped Scan Statistic for Real-Time Syndromic Surveillance 177

Paul Belanger and Kieran Moore

10 Mapping the Distribution of Malaria: Current Approaches andFuture Directions 189

Leah R. Johnson, Kevin D. Lafferty, Amy McNally, Erin Mordecai, KrijnP. Paaijmans, Samraat Pawar, and Sadie J. Ryan

11 Statistical Modeling of Spatiotemporal InfectiousDisease Transmission 211

Rob Deardon, Xuan Fang, and Grace P.S. Kwong

12 Spatiotemporal Dynamics of Schistosomiasis in China:Bayesian-Based Geostatistical Analysis 233

Zhi-Jie Zhang

13 Spatial Analysis and Statistical Modeling of 2009 H1N1 Pandemicin the Greater Toronto Area 247

Frank Wen, Dongmei Chen, and Anna Majury

14 West Nile Virus Mosquito Abundance Modeling UsingNonstationary Spatiotemporal Geostatistics 263

Eun-Hye Yoo, Dongmei Chen, and Curtis Russel

CONTENTS vii

15 Spatial Pattern Analysis of Multivariate Disease Data 283

Cindy X. Feng and Charmaine B. Dean

PART IV GEOSIMULATION AND TOOLS FOR ANALYZINGAND SIMULATING SPREADS OF INFECTIOUSDISEASES

16 The ZoonosisMAGS Project (Part 1): Population-BasedGeosimulation of Zoonoses in an Informed Virtual GeographicEnvironment 299

Bernard Moulin, Mondher Bouden, and Daniel Navarro

17 ZoonosisMAGS Project (Part 2): Complementarity of aRapid-Prototyping Tool and of a Full-Scale Geosimulator forPopulation-Based Geosimulation of Zoonoses 341

Bernard Moulin, Daniel Navarro, Dominic Marcotte, Said Sedrati, andMondher Bouden

18 Web Mapping and Behavior Pattern Extraction Tools to AssessLyme Disease Risk for Humans in Peri-urban Forests 371

Hedi Haddad, Bernard Moulin, Franck Manirakiza, Christelle Meha,Vincent Godard, and Samuel Mermet

19 An Integrated Approach for Communicable Disease GeosimulationBased on Epidemiological, Human Mobility and PublicIntervention Models 403

Hedi Haddad, Bernard Moulin, and Marius Theriault

20 Smartphone Trajectories as Data Sources for Agent-basedInfection-spread Modeling 443

Marcia R. Friesen and Robert D. McLeod

Index 473

Foreword: InterdisciplinaryCollaborations for Informed Decisions

When the unexpected occurs, decision makers scramble to understand the immediatethreat and to respond as best they can. The many disciplines, subgroups, and commu-nities of the science world may feel that their contribution is not fully appreciated orvalued. There is often much lip service to the value of interdisciplinary collaboration,but actual practice lags.

This book is strong evidence for making investments before the crisis, for favor-ing interdisciplinary collaborations, and for building long-term partnerships acrosssectors. In January 2008, a group of nine researchers from a diverse range of disci-plines pulled together a proposal to build a network of collaboration on the themeof infectious disease spread. This group included specialists in human and animalhealth, medical geography, and various modeling disciplines (mathematics, statistics,computer science, geomatics). Like many experienced research groups, they soughtsupport from various sources, and were successful with two Networks of Centres ofExcellence: MITACS and GEOIDE. As Scientific Director of GEOIDE at that time,I took this proposal alongside the 19 others submitted (pruned down in a preliminaryround from 44 expressions of interest).

Decisions are always easy in retrospect, when the results are known. It is hard toknow if a collection of disparate researchers can pull together to collaborate on a full-scale project. At that point, 9 years of experience at the GEOIDE Network had givenus a sense of how collaborations actually operate. This proposal was selected, througha pilot phase to become one of eight principal projects in Phase IV, the final fundingperiod, of the GEOIDE Network. The GEOIDE Board of Directors had adopted ahigher risk strategy of providing larger grants to fewer teams. Consequently, a pilotphase was put in place to provide a bit of assurance that the risk was worthwhile. Thisbook provides the proof that the funding decision was prudent. Canada and the Worldhave benefitted from the research efforts of the original team of nine, augmented overthe years through other funding sources.

Their proposal talked about a prudent scientific strategy starting with vector diseasespread for West Nile virus, Lyme disease, and avian influenza, leading up to pandemic

ix

x FOREWORD

influenza. In 2008, this last item was a potential threat with an unknown time horizon.The others had tangible outbreaks, of varying size and mechanisms. They weretherefore the first targets. As I flew around the world in 2009, public health authoritieswere nervously meeting airplanes with thermal cameras to attempt to react to the rapidspread of H1N1. Canada ramped up massive vaccinations projects in some provinces,and authorities around the world focused on the emerging threat. The project teamshowed great flexibility in responding, joining up with other teams around the worldto understand the process and to provide guidance for decisions. Already the valueof interdisciplinary collaboration was evident, and Canada played a key role inresponding to the international developments. Some of these chapters show how theteam responded to the changing circumstances for each of their respective diseasecontexts.

Interdisciplinary work is hard, since the rules of academic research vary across thedisciplines. But the work of understanding disease spread is not the sole proprietaryof any one group. Innovative approaches require fresh ways of looking as well astime to understand the contribution of others. This book brings together a varietyof techniques, each developed from many years’ effort in one of the contributingdisciplines. These approaches were put to a realistic test, through connection topartners in public health agencies and front-line hospital and clinic settings. The resultwill enrich each participant, and provide a basis for informed decisions. That was themission of GEOIDE, and this book provides additional proof that our investmentsare yielding benefits beyond the lifetime of the Network.

Nicholas Chrisman

RMIT University, Melbourne, Australia

Acknowledgements

This book is one of the outcomes of the project “Geosimulation Tools for SimulatingSpatial-temporal Spread Patterns and Evaluating Health Outcomes of Communica-ble Diseases”. This project was jointly supported by two centers of the CanadianNetwork of Centres of ExcellenceProgram: Geomatics for Informed Decision(GEOIDE) and Mathematics of Information Technology and Complex Systems(MITACS), in collaboration with the Public Health Agency of Canada (PHAC),Institut national de sant publique du Qubec, and Ontario Centre of Excellence. Themajority of chapters in this book come froma networkof researchers and their collab-orators on infectious disease modelinginitiated through this project. We would firstand foremost like to thank GEOIDE scientific director, Professor Nicholas Chrisman,for his encouragement, insight, and support to our interdisciplinary research, in gen-eral, and to this book project, in particular. We would also like to thank the GEOIDEResearch Management Committee for continuing support and constructive feedbackon our project.

This book is a collaborative work of many researchers, students, trainees, and staffmembers. We would like to thank all authors for contributing to the high quality ofthis book and timely revisions. We would also like to thank six anonymous reviewersand senior editor, Susanne Steitz-Filler, for their confidence in our initial proposaland for the critical comments and professional service that brought this manuscriptinto a special book published by John Wiley & Sons. We would also like to thank SariFriedman, Ho Kin Yunn, Baljinder Kaur, Mingjie Song, and Hong Yao for assistingus in collecting, formatting, and editing the chapters.

xi

Editors

Dongmei Chen is an associate professorat the Department of Geography, Queen’sUniversity at Kingston, Canada. She is alsocross-appointed at the Department of Envi-ronmental Study. She got her PhD in Geog-raphy from the Joint Doctoral Program ofSan Diego State University and University ofCalifornia at Santa Barbara, USA. Her mainresearch areas are geographic informationscience, remote sensing, spatial analysis, andtheir applications in environment and pub-lic health. She has lead and co-led more than20 environmental- and health-related researchprojects funded by federal and provincial gov-ernmental agencies and industry partners. Shehas coauthored over 80 peer-reviewed publi-cations and coedited two books.

Bernard Moulin is a full professor at LavalUniversity, Quebec, Canada. He is teachingin the Computer Science and Software Engi-neering Department. He is also a member ofthe Research Centre in Geomatics at LavalUniversity. He leads several research projectsin various fields: multi-agent- and population-based geo-simulation; design methods formulti-agent systems and software-agent envi-ronments; representation of temporal and spa-

tial knowledge in discourse; modeling and simulation of conversations between arti-ficial agents; modeling and design approaches for knowledge-based systems and

xiii

xiv EDITORS

multi-agent systems; as well as several projects at the intersection of geomatics andartificial intelligence. These research projects are (have been) funded by the NaturalScience and Engineering Council of Canada, the Canadian Network of Centres ofExcellence in Geomatics GEOIDE, Institut national de sante publique du Quebec, theNational Defense (Canada), and several other organizations and private companies.He has coauthored more than 340 peer-reviewed papers (journals, book chapters,international conferences), three books and coedited five books.

Jianhong Wu was endowed with the Univer-sity Distinguished Research Professorship atYork University in 2012, and he has been hold-ing a Tier I (Senior) Canada Research Chairin Industrial and Applied Mathematics since2001. His main research interest includes non-linear dynamics, data clustering, spatial ecol-ogy and disease modeling, and their interface.He has authored or coauthored eight books,coedited 12 special volumes/monographs, andover 300 peer-reviewed publications. He wasawarded the Queen’s Diamond Jubilee medalin 2012; the 2010 Award of Merit by the Feder-ation of Chinese Canadian Professionals Edu-cation Foundation; 2008 New Pioneer Science& Technology Award by Skills for Change; theCheung Kong Visiting Professor by the Min-istry of Education, P.R. China (2005–2008);

the FAPESP Visiting Researchers Fellowship, Brazil (2004); the Paul Erdos Vis-iting Professorship of the Hungarian Academy of Science (2000); and the Alexandervon Humboldt Fellowship, Germany (1996–1997). He is the founding Director of theCentre for Disease Modelling, and has led a few interdisciplinary research projectsincluding the MITACS disease modeling project and the GEOIDE geosimulations ofdisease spread project.

Contributors

Ahmed Abdelrazec, PhD, Department of Mathematics and Statistics, York Univer-sity, 4700 Keele Street, Toronto, ON M3J 1P3, Canada

Julien Arino, PhD, Associate Professor, Department of Mathematics, University ofManitoba, Winnipeg, Manitoba, R3T 2N2, Canada

Amit K. Banerjee, PhD, CSIR-Senior Research Fellow, Bioinformatics Group,Biology Division, CSIR-INDIAN INSTITUTE OF CHEMICAL TECHNOLOGY,Hyderabad 500 607, Uppal Road, Tarnaka, Andhra Pradesh, India

Paul Belanger, PhD, Manager, Public Health Informatics, KFL&A Public Health,221 Portsmouth Avenue, Kingston, ON, CANADA K7M1V5, and Adjunct Professorof Geography, Adjunct Professor of Public Health Sciences, Queen’s University,Kingston, ON, CANADA K7L3N6

Mondher Bouden, PhD, PhD student, Departement d’informatiqueet de genie logi-ciel, Universite Laval, Pavillon Pouliot, 1065, rue de la Medecine, Quebec QC G1V0A6, Canada

Lydia Bourouiba, PhD, Assistant Professor, Department of Mathematics, Mas-sachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA

Yurong Cao, Doctoral Student, Department of Mathematics and Statistics, YorkUniversity, 4700 Keele Street, Toronto, ON M3J 1P3, Canada

Dongmei Chen, PhD, Associate professor, Laboratory of Geographic Informationand Spatial Analysis, Department of Geography, Queen’s University, Kingston, ONK7L 3N6, Canada

Charmaine B. Dean, PhD, Professor, Department of Statistical and Actuarial Sci-ences & Faculty of Science, The University of Western Ontario, London, Ontario,N6A 3K7, Canada

xv

xvi CONTRIBUTORS

Rob Deardon, PhD, Associate Professor, Department of Mathematics & Statistics,University of Guelph, Guelph, Ontario N1G 2W1, Canada

Xuan Fang, MSc, Department of Mathematics & Statistics, Room 539 MacNaughtonBuilding, University of Guelph, Guelph, Ontario N1G 2W1, Canada

Cindy X. Feng, PhD, Assistant Professor, School of Public Health and Western Col-lege of Veterinary Medicine, University of Saskatchewan, Health Sciences Building,107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada

Marcia R. Friesen, PhD, P.Eng., Assistant Professor, Design Engineering, E2-262Engineering & Information and Technology Complex, University of Manitoba, Win-nipeg, MB R3T 2N2 Canada

Hedi Haddad, PhD, Assistant Professor, Computer Science Department, DhofarUniversity, P.O.Box : 2509 | Postal Code: 211, Salalah, Sultanate Of Oman

Daozhou Gao, PhD, Postdoctoral Scholar, Francis I. Proctor Foundation for Researchin Ophthalmology, University of California, San Francisco, CA 94143, USA

Xin Gao, PhD, Associate Professor, Department of Mathematics and Statistics, YorkUniversity, 4700 Keele Street, Toronto, ON M3J 1P3, Canada

Vincent Godard, PhD, Professor, Universite de Paris 8, UFR TES – Departementde Geographie, 2, Rue de la Liberte, F-93526 Saint-Denis Cedex 02

Stephen Gourley, PhD, Professor, Department of Mathematics, University of Surrey,Guildford, Surrey GU2 7XH, UK

Leah R. Johnson, PhD, Assistant Professor, Department of Integrative Biology,University of South Florida, 4202 East Fowler Ave, SCA110, Tampa, FL 33620

Kamran Khan, MD, Scientist and Associate Professor, Centre for Research on InnerCity Health, St Michael’s Hospital, Department of Medicine, Division of InfectiousDiseases, Department of Health Policy, Management and Evaluation, University ofToronto, Toronto, Ontario M5B 1W8, Canada

Grace P.S. Kwong, PhD, Postdoctoral Research Statistician, Department of Popu-lation Medicine, Ontario Veterinary College, University of Guelph, Guelph, OntarioN1G 2W1, Canada

Kevin D. Lafferty, PhD, Research ecologist, adjunct faculty, US Geological Survey,Marine Science Institute, University of California, California, Santa Barbara, CA93106

Rongsong Liu, PhD, Assistant Professor, Department of Mathematics & Program inEcology, University of Wyoming, Laramie, WY 82071, USA

Robert D. McLeod, PhD, P.Eng., Professor, Electrical & Computer Engineering,E2-390 EITC, Engineering & Information and Technology Complex, University ofManitoba, Winnipeg, MB R3T 2N2 Canada

CONTRIBUTORS xvii

Anna Majury, MD, Regional microbiologist and assistant professor, Public HealthOntario Laboratories – Eastern Ontario and Queen’s University, Kingston, PublicHealth Ontario | Sante publique Ontario, 181 Barrie Street| 181 Rue Barrie, Kingston,ON, K7L 4V6

Franck Manirakiza, Msc Student, Departement d’informatiqueet de genie logiciel,Universite Laval, Pavillon Pouliot, 1065, rue de la Medecine, Quebec QC G1V 0A6,Canada

Dominic Marcotte, Research professional, Departement d’informatiqueet de genielogiciel, Universite Laval, Pavillon Pouliot, 1065, rue de la Medecine, Quebec QCG1V 0A6, Canada

Christelle Meha, PhD student in geography, UMR 8185 ENeC Paris IV-CNRS,Maison des Sciences de l’Homme Paris Nord, 4 rue de la Croix Faron, F-93210Saint-Denis La Plaine

Samuel Mermet, Assistant researcher in database management, LVMT – UniversiteParis-Est, 6 et 8, avenue Blaise Pascal, Cite Descartes Champs-sur-Marne, F-77455Marne-la-Vallee

Amy McNally, PhD student, Department of Geography, Climate Hazards Group,UC Santa Barbara, Santa Barbara, CA 93106-4060

Erin Mordecai, PhD, Postdoctoral Fellow, Department of Biology. The Universityof North Carolina at Chapel Hill. Chapel Hill NC 27599-3280

Kieran Moore, MD, Associate Medical Officer of Health, KFL&A Public Health,Adjunct Professor of Emergency Medicine, Queen’s University, Kingston, ON,CANADA K7M1V5

Bernard Moulin, PhD, Professor, Departement d’informatiqueet de genie logiciel,Universite Laval, Pavillon Pouliot, 1065, rue de la Medecine, Quebec QC G1V 0A6,Canada

U.S.N. Murty, PhD, Director Grade Scientist, Head, Biology Division, CSIR-INDIAN INSTITUTE OF CHEMICAL TECHNOLOGY, Hyderabad 500 607, UppalRoad, Tarnaka, Andhra Pradesh, India

Daniel Navarro, Msc Student, Departement d’informatiqueet de genie logiciel, Uni-versite Laval, Pavillon Pouliot, 1065, rue de la Medecine, Quebec QC G1V 0A6,Canada

Krijn P Paaijmans, PhD, Assistant research professor, Barcelona Centre for Inter-national Health Research (CRESIB, Hospital Cl ınic-Universitat de Barcelona),Barcelona, E-08036, Spain

Samraat Pawar, PhD, Lecturer, Division of Ecology and Evolution, Imperial CollegeLondon, Silwood Park Campus, Ascot, Berkshire SL5 7PY, United Kingdom

Paul Proctor, Peel Public Health, Mississauga, ON, Canada

xviii CONTRIBUTORS

Heather Richardson, Undergraduate Student, Department of Geography, Queen’sUniversity, Kingston, ON K7L3N6, Canada

Curtis Russell, PhD, Program Consultant, Enteric, Zoonotic and Vector-Borne Dis-eases, Public Health Ontario | Sante publique Ontario

Sadie J. Ryan, PhD, Assistant Professor, Department of Environmental and ForestBiology and Division of Environmental Science, College of Environmental Scienceand Forestry, State University of New York, Syracuse, New York, 13210 USA, andCenter for Global Health and Translational Science, Department of Immunologyand Microbiology, Upstate Medical University, Syracuse, New York, 13210, USA,and College of Agriculture, Engineering and Science, University of Kwazulu-Natal,King Edward Avenue, Scottsville, Pietermaritzburg, Private Bag X01, Scottsville,3209, South Africa

Shigui Ruan, PhD, Professor, Department of Mathematics, University of Miami,Coral Gables, FL 33124, USA

Said Sedrati, Msc Student, Departement d’informatiqueet de genie logiciel,Universite Laval, Pavillon Pouliot, 1065, rue de la Medecine, Quebec QC G1V0A6, Canada

John Takekawa, PhD, Research wildlife biologist, USGS Western EcologicalResearch Center, San Francisco Bay Estuary Field Station, 505 Azuar Drive, Vallejo,CA 94592 USA

Marius Theriault, PhD, Professor, Ecole superieure d’amenagement du territoireet de developpement regional (Graduate School of Regional Planning and Develop-ment), Centre de recherche en amenagement et developpement, Centre de rechercheen geomatique, Universite Laval, Quebec G1K 7P4, Canada

Frank Wen, MSc student, Department of Geography, Queen’s University, Kingston,ON K7L3N6, Canada

Jianhong Wu, PhD, University Distinguished Research Professor, Canada ResearchChair in Industrial and Applied Mathematics, Director, Centre for Disease Modeling,Department of Mathematics and Statistics, York University, Toronto, Ontario, CanadaM3J 1P3

Eun-Hye Yoo, PhD, Assistant Professor, Department of Geography, University atBuffalo, The State University of New York (SUNY), 121 Wilkeson Quad, NorthCampus, Buffalo, NY, 14261-0055

Zhi-Jie Zhang, PhD, Associate Professor, Department of Epidemiology and Bio-statistics, Laboratory for Spatial Analysis and Modeling, Biomedical Statistical Cen-ter, School of Public Health, Fudan University, Shanghai, 200032, China

Huaiping Zhu, PhD, Professor, Department of Mathematics and Statistics, YorkUniversity, Toronto, ON M3J 1P3, Canada

P A R T I

Overview

C H A P T E R 1

Introduction to Analyzing andModeling Spatial and TemporalDynamics of Infectious DiseasesDongmei ChenDepartment of Geography, Faculty of Arts and Science,Queen’s University, Kingston, ON, Canada

Bernard MoulinDepartment of Computer Science and Software Engineering, Faculty ofScience and Engineering, Laval University, Quebec City, QC, Canada

Jianhong WuDepartment 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 thestatistics of the World Health Organization (WHO), 25% of human death is caused byinfectious diseases. The spread of an infectious disease involves characteristics of theagent such as virus and bacteria, the host, and the environment in which transmissionstake place. Appropriately modeling and actually predicting the outcome of diseasespread over time and across space is a critical step toward informed development ofeffective 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 developappropriate analysis methods, models, and tools to assess and predict the disease

Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases, First Edition.Edited by Dongmei Chen, Bernard Moulin, and Jianhong Wu.© 2015 John Wiley & Sons, Inc. Published 2015 by John Wiley & Sons, Inc.

3

4 INTRODUCTION TO ANALYZING AND MODELING SPATIAL

spread and evaluate the disease risk. In order to ensure better understanding andto design more effective strategies for responding to existing and future diseaseoutbreaks, questions such as the following are often asked:

(a) What are the distributions of diseases across space and how do they interactwith their environment? What are their origins, destinations, and spreadingchannels?

(b) What are the potential spreading patterns of a disease across space and overtime given the potential habitats of its host and its environment?

(c) Which diseases will be spread around the globe successfully via global trav-eling and trading as well as wildlife movement (e.g., bird migration)?

(d) Which parts of regions (or cities) are at the greatest risk of being exposed toa disease given urban and regional host habitats and population distributionsas well as intercity and regional transportation networks?

(e) Which population groups are most vulnerable to a disease?

Understanding the spatiotemporal patterns of disease spread is the key to identi-fying effective prevention, control, and support of infectious diseases. Recognizingthe conditions under which an epidemic may occur and how a particular diseasespreads is critical to designing and implementing appropriate and effective publichealth control measures.

Methods and tools are needed to help answer aforementioned questions involvingthe spatiotemporal patterns, their relevance and implications to humans and ecosys-tems, their impact on the vulnerability of different populations, and to develop publichealth policy decisions on disease prevention issues. Multidisciplinary collaborationamong experts on different aspects of these diseases is important to develop andutilize these tools.

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

Through a research project jointly funded by the Canadian Network of Centersof Excellence on Geomatics for Informed Decision (GEOIDE), Mathematics ofInformation Technology and Complex Systems (MITACS), Public Health Agencyof Canada (PHAC), and Institut national de sante publique du Quebec, a networkof more than 30 researchers coming from academics, government agencies, and

INFECTIOUS DISEASES, THEIR TRANSMISSION AND RESEARCH NEEDS 5

industry in Canada, the United States, France, China, India, and other countries wasestablished in 2008 and has since been conducting collaborative projects in selecteddiseases representing different modes of transmission dynamics. This network hasalso organized several workshops on spatial and temporal dynamics of infectiousdiseases.

This book represents a collection of most recent research progresses and col-laboration results from this network of researchers and their collaborators. Twentychapters contributed by fifty researchers in academic and government agencies fromseven countries have been included in this book. As such, the book aims to capture thestate-of-art methods and techniques for monitoring, analyzing, and modeling spatialand temporal dynamics of infectious diseases and showcasing a broad range of thesemethods and techniques in different infectious disease studies.

In the following, we give a brief overview of infectious diseases and the transmis-sion mechanisms of different infectious diseases covered in this book, followed byoutlining the structure and contents of this book.

1.2 INFECTIOUS DISEASES, THEIR TRANSMISSIONAND RESEARCH NEEDS

Infectious diseases are also known as transmissible diseases or communicable dis-eases. The illness of infectious diseases is caused by the infection, presence, andgrowth of pathogenic biological agents (known as pathogens) in an individual hostorganism. Pathogen is the microorganism (or microbe) that causes illness. Infectiouspathogens include viruses, bacteria, fungi, protozoa, multicellular parasites, and aber-rant proteins known as prions. These pathogens are the cause of disease epidemics,in the sense that without the pathogen, no infectious epidemic occurs. The organismthat a pathogen infects is called the host. In the human host, a pathogen causes illnessby either disrupting a vital body process or stimulating the immune system to mounta defensive reaction (www.metrohealth.org). Based on the frequency of occurrence,infectious diseases can be classified as sporadic (occurs occasionally), endemic (con-stantly 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 oneperson to another. The transmission mechanisms of infectious diseases can becategorized as contact transmission, vehicle transmission, and vector transmission.Contact transmission can occur by direct contact (person-to-person) between thesource of the disease and a susceptible host, indirect contact through inanimateobjects (such as contaminated soils), or droplet contact via mucus droplets incoughing, sneezing, laughing or talking. Vehicle transmission involves a media.Based on the media type in transmission, the infectious diseases can be categorizedas airborne (diseases transmitted through the air such as influenza, anthrax, measles),foodborne (diseases transmitted through the foods such as Hepatitis A and E), andwaterborne (diseases transmitted through the water such as Cholera).

6 INTRODUCTION TO ANALYZING AND MODELING SPATIAL

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 hostto another (James 2001). Vectors may be mechanical or biological. A mechanicalvector picks up infectious pathogens outside of its body and transports them in apassive manner through its movement (such as housefly). The pathogen never entersor impacts the body of the vector. On the contrary, a biological vector lets the pathogenreproduce in its body. Most commonly known biological vectors are arthropods suchas mosquitoes, ticks, flies, and bugs. Many biological vectors feed on blood at someor all stages of their life cycles. During the blood feeding, the pathogens enter thebody of the host and cause the illness.

Understanding the disease transmission mechanism is important for infectiousdisease control and prevention. Many factors can influence the spreading patterns ofinfectious diseases. For diseases with different transmission mechanisms, factors thatcan impact the disease spread vary. Human mobility and social networks can greatlyimpact the spread of infectious diseases with contact transmission. Climate andenvironmental conditions can significantly impact the habitat suitability, distribution,and abundance of vectors. Climate change can influence survival and reproductionrates of vectors and pathogens within them, as well as intensity and temporal patternof vector activity throughout the year. Human activities such as land use change,habitat disruption, pesticide use can significantly change the vector habitat and mediacondition, and thus impact the spread of diseases.

Quantitatively analyzing and modeling spreading of infectious diseases underdifferent environmental and climate conditions is not new. Many methods andapproaches have been developed to simulate infection process, investigate observeddisease patterns, and predict future trends (see Chapter 2 in this book). Much ofthe past effort on disease modeling has been devoted to mathematical modeling atpopulation level assuming various kinds of homogeneity. However, possible spatial–temporal spread and outcomes of a disease outbreak at different communities andenvironments usually play even more important roles in determining public healthinterventions. Spatial analysis, modeling, and simulation of infectious disease trans-mission provide a plausible experimental system in which information of hosts andvectors and their typical movement patterns can be combined with a quantitativedescription of the infection process and disease natural history to investigate observedpatterns and to evaluate alternative intervention options (Riley 2007).

There are roughly three stages in predicating the transmission of an infectiousdisease (Rogers and Randolph 2006): (1) identification of the pathogen, its host, andits pathway of transmission among the hosts; (2) determining the spatial transmissionpattern of infectious diseases and their environment; (3) understanding the dynamicprocess of the transmission of the disease using models. However, each of thesestages involves significant challenge. The first stage requires effective diagnostictools and initial exploration of the disease. The second stage involves the survey andquantitative description of the spatial and temporal pattern of the disease, followedby analyzing the relationship of the disease with its environment. The goal of thethird stage is to establish quantitative models calibrated with field measurements andsurveys.

DISEASES COVERED IN THIS BOOK AND THEIR TRANSMISSION MECHANISM 7

1.3 DISEASES COVERED IN THIS BOOK AND THEIRTRANSMISSION MECHANISM

In this book several diseases with different disease spread mechanisms, includingWest Nile virus, Lyme disease, influenza, schistosomiasis, malaria, sexually trans-mitted diseases, have been used for various analyzing, modeling, and simulationapplications in different chapters. Here we briefly outline the transmission process,pathogen, host, and main vectors for each disease.

1.3.1 West Nile Virus

West Nile virus (WNV) is a vector-borne disease with the virus belonging to thegenus Flavivirus in family Flaviviridae. WNV is known to be transmitted to humansthrough the bite of an infected mosquito. It was first identified in Uganda in EastAfrica in 1937 and had been a sporadic disease before the mid-1990s. The first largeoutbreak of WNV was in Romania in 1996. Since then WNV has spread globally andbecomes endemic in Africa, Europe, West Asia, North America, and the Middle East.WNV first appeared in the United States in 1999 (Nash et al. 2001) and had spreadfrom New York State to all the 48 continental states of the United States between 1999and 2005. In Canada, WNV was first detected in Ontario and Quebec in 2001 and hadspread to seven provinces of Canada by 2003. WNV can cause neurological diseaseand death in humans. In 2012 alone, the United States had 5674 WNV human casesreported (CDC 2013a), in which 92% cases had illness on-site and 5% (286) died.

WNV is commonly transmitted to humans by female mosquitoes, the primevector, and it is maintained in nature through a cycle involving transmissionbetween birds and mosquitoes (see the picture at http://www.westnile.state.pa.us/animals/transmission.htm for WNV’s transmission cycle). Birds are primaryreservoir for WNV. In North America, there are over 17 native bird species thatcan carry WNV. The WNV-carrying mosquito species vary at different geographicalareas. On the east coast of North America, Culex pipiens is the main source, while themain species in the Midwest and West are Culex tarsalis and Culex quinquefasciatusin the Southeast (Hayes et al. 2005). Humans, horses, and other mammals can beinfected. When a mosquito bites an infected bird, the virus enters the mosquito’sbloodstream. When an infected mosquito bites an animal or a human, the virus ispassed into the host’s bloodstream and causes serious illness of the host. About 20%of people who become infected with WNV will develop West Nile fever.

1.3.2 Lyme Disease

Lyme borreliosis, more commonly referred to as Lyme disease, is a tick-borne diseasecaused by the bacterium belong to the genus Borrelia (Samuels and Radolf 2010).Borrelia burgdorferi is the main bacteria type of Lyme disease in North America.This disease can be transmitted to humans by the bite of certain infected ixodid ticks.Unlike mosquitoes that can transfer WNV to humans with a single bite, the tick has tobe attached to the body for at least 24–36 hours. One of the most prominent symptoms

8 INTRODUCTION TO ANALYZING AND MODELING SPATIAL

of Lyme is a skin lesion, known as erythema migrans. Closely resembling a bull’s-eye,this rash can expand up to the width of a person’s back from the site of the tick bite.This disease can also cause flu-like symptoms such as fever, headache, and musclepain at its early stages. Without proper treatment, the bacterium can disseminate toother tissues, affecting joints, neurologic, and cardiac systems (PHAC 2014). Lymeborreliosis has become endemic in many areas of Asia, Europe, and North Americaand is the most commonly reported vector-borne illness in the United States with atotal number of 22,014 confirmed cases in 2012 (http://www.cdc.gov/Lyme/stats/).

The life cycle of the tick undergoes three main developmental stages consistingof larva, nymph, and adult (see Figure 18.1). The life span of the tick range fromapproximately 2 to 4 years and their development rate depends on the time it takesfor the tick to find a host to feed on as a larva or nymph. While ticks may acquirethe bacterium at any time during their life cycle and transfer the disease from onedevelopmental stage to the next (Spielman et al. 1985), studies have shown thatthe tick will not transfer the bacterium vertically via egg from an infected female(Magnarelli et al. 1987). Adult ticks tend to feed and mate on medium- to large-sizedmammals, such as humans, white-tailed deer (Odocoileus virginianus), dogs, cats,raccoons, bears, and horses (Morshed et al. 2006). As the tick depends on a variety ofmammalian hosts for their method of transportation, the spatial distribution of Lymedisease is highly dependent on the spatial variation of its hosts such as small rodents,white-tailed deer, and migratory birds to expand their range (Odgen et al. 2008).

1.3.3 Avian and Human Influenza

Influenza (commonly known as the flu) is a common respiratory disease for birdsand mammals caused by RNA (ribonucleic acid) viruses. For humans, the flu viruscan be easily passed from person to person and affects the nose, throat, and lungs.The common flu symptoms include fever, runny nose, headaches, coughing, fatigue,muscle pain, and other illness (Eccles 2005).

There are three main types of influenza viruses (A, B, and C), in which A is themain cause of influenza in humans and can cause severe human pandemic (MacKellar2007). Most influenza virus that caused human pandemic deaths in the history aretype A virus, such as H1N1 (which caused Spanish flu in 1918 and Swine flu in2009), H2N2 (which caused Asian flu in1957), and H7N9 (2013 in China). Wildaquatic birds are the natural hosts for a large variety of influenza A strains. Influenzais transmitted through the air. When an infected person coughs or sneezes, infecteddroplets containing the virus get into the air and another person can breathe themin and get exposed. The virus can also be spread by hands infected with the virus.Influenza can also be transmitted by direct contact with bird droppings or nasalsecretions containing the virus, or through contact with contaminated surfaces.

RNA viruses have been recognized as highly mutable since the earliest studies,and responsible for a variety of medically and economically important diseases ofman, plants, and animals (Steinhauer and Holland 1987). Based on the WHO’sreport, seasonal influenza cause about 3–5 million cases severe illness and about250,000–500,000 deaths each year. A deadly human influenza pandemic would cause

DISEASES COVERED IN THIS BOOK AND THEIR TRANSMISSION MECHANISM 9

2–7.4 million deaths worldwide over the course of around 3 months (WHO 2009),and the World Bank estimated that the potential economic cost of a pandemic ofhuman influenza would be as much as US$2 trillion in damages.

All birds are thought to be susceptible to infection with bird flu (or avian) influenzaviruses. Depending on the virus strain type, influenza virus can also cause devastatingoutbreaks in domestic poultry or wild birds. For example, highly pathogenic H5N1bird-flu virus had hit 53 countries since 2003, caused over 3200 million domestic andwild birds to be killed at a cost of well over US$20 billion, and ruined the livelihoodof millions of smallholder farmers. To date (October 2013), H5N1 also caused 641human illnesses in which 380 died (WHO 2013d).

1.3.4 Schistosomiasis

Schistosomiasis is a water-borne parasitic disease caused by blood flukes (trematodeworms) of the genus Schistosoma. There are two major forms of schistosomiasis(intestinal and urogenital) caused by five main species of blood fluke, impactingdifferent geographical regions of the world (WHO 2013b). Schistosomiasis oftencauses chronic illness that can damage internal organs. Intestinal schistosomiasiscan result in abdominal pain, diarrhea, and blood in the stool. Liver enlargementis common in advanced cases. The classic sign of urogenital schistosomiasis ishematuria (blood in urine). Fibrosis of the bladder and ureter and kidney damage aresometimes seen in advanced cases (WHO 2013b).

Schistosomiasis is transmitted through snails in the water as the intermediaryagent with humans being the definitive host (see its life cycle at http://commons.wikimedia.org/wiki/File:Schistosomiasis_Life_Cycle.jpeg). Fresh water contami-nated by parasites is the main media of Schistosomais spreading. Larval forms ofthe parasite released by snails in the freshwater can penetrate the human skin whenpeople contact infested water. Schistosomiasis is the second most socioeconomicallydevastating parasitic disease after malaria and has been reported in 78 countries. Therewere about 28.1 million of people reported to have been treated for schistosomiasisin 2011 (WHO 2013b).

1.3.5 Malaria

Malaria is a vector-borne infectious disease caused by the protozoan parasites of thegenus Plasmodium. The malaria parasite is transmitted to humans via the bites ofinfected female mosquitoes of the genus Anopheles. Of the hundreds of Anophelesspecies described, approximately 70 have been shown to be competent vectors ofhuman malaria (Hayes et al. 2005). Mosquitoes can become infected when theyfeed on the blood of infected humans. Thus, the infection goes back and forthbetween humans and mosquitoes. Malaria causes symptoms that typically includefever and headache, which in severe cases can progress to coma or death. The diseaseis widespread in more than 100 countries in Africa, Southeast Asia, the EasternMediterranean, Western Pacific, Americas, and Europe, in which most are in tropicaland subtropical regions around the equator.

10 INTRODUCTION TO ANALYZING AND MODELING SPATIAL

Malaria can cause significant economic loss and enormous public health problems.Half of the world population is at risk of malaria (WHO 2013a). In 2010 there werean estimated 219 million malaria cases, with an estimated 660,000 deaths, of which90% occurred in sub-Saharan Africa and the majority were children under five inSub-Saharan Africa (WHO 2013a).

1.3.6 Sexually Transmitted Diseases

Sexually transmitted diseases (STDs) are also referred to as sexually transmittedinfections (STIs) and venereal diseases (VDs). There are more than 20 types of STDscaused by 30 different bacterial, fungal, viral, or parasitic pathogens (CDC 2013b).STD transmission in human population is mainly caused by person-to-person sexualcontact. Some STIs can also be transmitted via IV drug needles used by an infectedperson, as well as through childbirth or breastfeeding.

STDs have a major negative impact on sexual and reproductive health worldwide.STDs are an important cause of infertility in men and women. According to WHO,499 million new cases of curable STIs (which do not include non-curable STDs suchas HIV) occur annually throughout the world in adults aged 15–49 years (WHO2013c). In the United States about 20 million new STD infections occur each year,in which half occur among young people aged 15–24 (CDC 2013b).

1.4 THE ORGANIZATION AND OUTLINE OF THIS BOOK

This book is organized into four parts with 20 chapters. It starts with an overviewchapter on various spatial modeling methods of infectious diseases, followed withthree sections of different mathematical, statistical, spatial modeling, and geosimu-lation techniques.

Part I begins with a brief overview of the background of infectious diseases,diseases covered in this book, and research needs of modeling and analyzing thespatial and temporal dynamics of infectious diseases. The second chapter, writtenby Chen, provides a general review of different methods of modeling spatial andspatial–temporal dynamics of infectious diseases. The advantages and limitations ofdifferent methods are compared, and issues and challenges in disease modeling arehighlighted.

1.4.1 Mathematical Modeling of Infectious Diseases

This part starts with Chapter 3 on a narrative about bioinformatics and mathematicalmodeling studies to understand the infection dynamics and spatial spread of WNV.Although WNV was isolated in 1937 and several outbreaks in different regions havebeen reported since then, this mosquito-borne disease has become a major publichealth issue in North America since its identification in New York City in 1999.The narrative provided by Murty, Banerjee, and Wu started with a brief reviewabout the epidemiology, disease transmission, viral genomics, and bioinformatics