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The GISVET’04 Special Edition
Preventive Veterinary Medicine 71 (2005) 137–140 www.elsevier.com/locate/prevetmed
Preface
The GISVET’04 Special Edition In September 2001, the first conference specifically devoted to the applications of GIS and spatial analyses to animal health, was held at Lancaster, England. The original purpose of this conference was to provide an opportunity for the authors of the chapters of a forthcoming book on specific themes of GIS applications to animal health to preview their work to an interested audience. Following on from the encouragement of the Editor-in-Chief of Preventive Veterinary Medicine, Dr. Hollis Erb, we opened up the conference to research papers, with the enticement that those that surmounted the hurdle of the normal peer review process, might be published in a Special Edition of the journal. Both the book (Durr and Gatrell, 2004) and the Special Edition (Durr and Pfieffer, 2002), arising from GISVET 2001 involved their share of considerable postconference work. The first GISVET conference was planned as a one-off event. Nevertheless, such was the success of the conference that most delegates expressed an interest in holding a second one, in three years time. Using the successful example of the conferences of the International Society of Veterinary Epidemiology & Economics (ISVEE) we decided to make it a movable feast, and hold it outside of the UK. Due mainly to the enthusiasm of Professor Rowland Tinline of Queens University at Kingston, Ontario, the location of the 2004 conference came to be Canada. Canada was, in terms of history, an ideal location for GISVET’04, being as it was the country that coined the term ‘‘geographical information system’’. As almost all textbooks on GIS recount, it was the Canadian Geographical Information System (CGIS) that provided the first example of how integrating various different sources of information (soil type, vegetation, topography, etc.) could provide a means for determining the potential productivity of the landscape for agriculture. Like so many pioneer systems, it was ultimately not entirely successful and it was left to others to further develop the GIS concept. The complexity of the computer technology that bedevilled the CGIS has meant that for a long time GIS was defined in terms of software, and indeed many veterinary users may see it simply as an application for drawing maps. Nevertheless, in the past 10–15 years, as many computing challenges have been resolved successfully, the emphasis has shifted to more fundamental questions, such as ways to retain the quality and currency of spatial data and a means to extract useful information and avoid erroneous 0167-5877/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2005.07.001
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Preface / Preventive Veterinary Medicine 71 (2005) 137–140
interpretation of results. Indeed, the scope of the challenges currently being tackled by GIS specialists has meant that increasingly many now prefer to replace ‘‘system’’ with ‘‘science’’. Many of these technical issues, such as the most meaningful use of colour and texture in computer generated maps, may seem somewhat arcane to veterinary epidemiologists simply wanting to draw a map to highlight areas of enhanced disease risk. However, the issue that impacts immediately after the map is drawn is how to undertake a statistical analysis of the data, as current GIS software provides few effective and robust tools for this purpose. In part, this derives from quantitative users being a small and specialist subset of the GIS market, but equally it is a result of the complexity of applying statistics to spatial data. The underlying problem is that of ‘‘spatial autocorrelation’’, the tendency for measurements close in space to be close in value. This is one of the tantalising paradoxes of spatial data, as almost always it leads to a result in terms of maps that identify clusters of disease or areas of apparently elevated risk. However, once one realises that such a ‘‘result’’ is the norm, the challenge then becomes how to meaningfully analyse and interpret it. Thus, the questions become, is there truly an elevated disease risk, or do the clusters simply reflect the distribution of the population? Could the clusters represent sampling biases, such that all one is seeing on the map is areas where sampling was possible? These questions can only be answered by a robust study design and thorough statistical analysis. Nonetheless, when one looks for the statistical routines and advice on how to use them, the challenging nature of spatial analysis becomes apparent (Pfieffer, 2000). Fortunately, the challenge of how to undertake spatial analyses of veterinary epidemiological data is one that has been taken up with enthusiasm by workers in the past few years, as becomes apparent when comparing this Special Edition with the previous one (Durr and Pfieffer, 2002). In the first special issue, only one paper could be highlighted as introducing a new statistical methodology that by Christoph Staubach, who used Bayesian hierarchical modelling to overcome problems of sampling bias while investigating the prevalence of pseudorabies virus in wild boar in eastern Germany. This innovative paper was a harbinger of greater use of Bayesian methodologies, one that will be familiar to readers of Preventive Veterinary Medicine in other fields of epidemiology, particularly the analysis and interpretation of laboratory test results. Bayesian approaches to data analysis are of course not new, but the problem has always been how to practically implement them for anything other than trivial problems. The development of the software package WinBUGS in the 1990s under the enlightened sponsorship of the Medical Research Council of the UK has made it possible to solve previously intractable analyses. This is fully reflected in the papers collated here, with Bayesian approaches used for spatial survival analyses (Sanchez et al.), spatial regression (Durr et al.), spatio-temporal analysis (Clements et al.) epidemic disease modelling (Lawson and Zhou), and modelling spatial risk (Stevenson et al.), all except the last implemented using WinBUGS. Of course, this rush to all things Bayesian poses many new challenges, and novice practitioners need to be warned of the dangers of using uninformative priors and undertaking insufficient iterations. But, there is no doubt that this approach has transformed, and will continue to transform, the type of analyses possible in spatial epidemiology.
Preface / Preventive Veterinary Medicine 71 (2005) 137–140
139
One of the encouraging aspects of the GISVET’04 conference was that it bought together a wide community of scientists interested in the use of GIS and spatial analyses to animal health problems, including statisticians, wildlife ecologists and human and veterinary epidemiologists. This feature is somewhat more apparent in our accompanying conference proceedings (Durr and Martin, 2004), but is reflected here in the two explicitly modelling papers, those by Michael Ward and David Smith et al., who tackle issues of the spread of West Nile Virus Disease in horses and rabies in racoons, respectively. Mathematical modelling of disease has tendered to suffer from its complexity and thus opacity to all but specialist practitioners, and this has frequently engendered suspicion from more traditional epidemiologists. Displaying modelling predictions on maps has the potential to make their usefulness (or otherwise) more obvious, and this is an area in which we predict that GIS will act as a bridge helping to span the gulf between mathematical and conventional epidemiology. Many people helped to enable GISVET’04 to take place and thus make possible this Special Edition. In particular, we would like to express our gratitude to the organizing committee of the conference in Guelph; in particular, Bruce McNab of the Ontario Ministry of Agriculture & Food, Victoria Edge of the Public Health Agency of Canada, Muriel Burke of the University of Guelph and Vincent Adcock of the Veterinary Laboratories Agency. Without their efforts, the conference would never have achieved the high standard that it did. To the Dean of the Ontario Veterinary College, the Chair of Population Medicine, the Chair of the Department of Geography, and the Conference Services at the University of Guelph, we send our thanks for their assistance. We are also grateful to Dirk Pfeiffer, Angus Cameron, Graeme Garner, Rowland Tinline, Michael Ward, Christoph Staubach, and others (who we may have temporarily forgotten) for helping us to select the research papers presented here. We also acknowledge the assistance of the Editorial Office of Preventive Veterinary Medicine and its Editor-inChief, Dr. Hollis Erb, in helping us deliver what we truly believe to be a superb collection of scientific papers. Finally, we would like to thank three organizations that helped with financial support of the conference; these include the Ontario Ministry of Agriculture and Food, Habit (H.A.B.I.T. Research Ltd., 692 Sumas Street, Vic., BC, Canada V8T 4S6, [email protected]), and ESRI (ESRI Canada, 49 Gervais Drive, Toronto, Ont., Canada M3C 1Y9, http://www.esricanada.com/).
References Durr, P., Gatrell, A. (Eds.), 2004. GIS and Spatial Analysis in Veterinary Science. CABI, Wallinford, Oxon, 303 pp. Durr, P.A., Pfieffer, D.U. (Eds.), 2002. Spatial epidemiology. Research papers prepared for and presented at the GISVET conference, Lancaster University, England, September 10–14, 2001. Prev. Vet. Med. 56, 1 (special ed.), 103 pp. Pfieffer, D.U., 2000. Spatial analysis—a new challenge for veterinary epidemiologists. In: Proceedings of the Society for Veterinary Epidemiology and Preventive Medicine, University of Edinburgh, March 29–31, pp. 86–106.
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Preface / Preventive Veterinary Medicine 71 (2005) 137–140
Durr, P.A., Martin, S.W. (Eds.), 2004. Proceedings of GISVET’04—Second International Conference on the Applications of GIS and Spatial Analysis to Veterinary Science, University of Guelph, June 23–25, VLA, Weybridge, England, 103 pp. http://www.gisvet.org/.
Peter Durr Veterinary Laboratories Agency, Weybridge, Surrey, England Wayne Martin* Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ont., Canada N1G 2W1 *Corresponding author. Tel.: +1 519 824 4120x54045; fax: +1 519 763 3117
Preface
The GISVET’04 Special Edition In September 2001, the first conference specifically devoted to the applications of GIS and spatial analyses to animal health, was held at Lancaster, England. The original purpose of this conference was to provide an opportunity for the authors of the chapters of a forthcoming book on specific themes of GIS applications to animal health to preview their work to an interested audience. Following on from the encouragement of the Editor-in-Chief of Preventive Veterinary Medicine, Dr. Hollis Erb, we opened up the conference to research papers, with the enticement that those that surmounted the hurdle of the normal peer review process, might be published in a Special Edition of the journal. Both the book (Durr and Gatrell, 2004) and the Special Edition (Durr and Pfieffer, 2002), arising from GISVET 2001 involved their share of considerable postconference work. The first GISVET conference was planned as a one-off event. Nevertheless, such was the success of the conference that most delegates expressed an interest in holding a second one, in three years time. Using the successful example of the conferences of the International Society of Veterinary Epidemiology & Economics (ISVEE) we decided to make it a movable feast, and hold it outside of the UK. Due mainly to the enthusiasm of Professor Rowland Tinline of Queens University at Kingston, Ontario, the location of the 2004 conference came to be Canada. Canada was, in terms of history, an ideal location for GISVET’04, being as it was the country that coined the term ‘‘geographical information system’’. As almost all textbooks on GIS recount, it was the Canadian Geographical Information System (CGIS) that provided the first example of how integrating various different sources of information (soil type, vegetation, topography, etc.) could provide a means for determining the potential productivity of the landscape for agriculture. Like so many pioneer systems, it was ultimately not entirely successful and it was left to others to further develop the GIS concept. The complexity of the computer technology that bedevilled the CGIS has meant that for a long time GIS was defined in terms of software, and indeed many veterinary users may see it simply as an application for drawing maps. Nevertheless, in the past 10–15 years, as many computing challenges have been resolved successfully, the emphasis has shifted to more fundamental questions, such as ways to retain the quality and currency of spatial data and a means to extract useful information and avoid erroneous 0167-5877/$ – see front matter # 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.prevetmed.2005.07.001
138
Preface / Preventive Veterinary Medicine 71 (2005) 137–140
interpretation of results. Indeed, the scope of the challenges currently being tackled by GIS specialists has meant that increasingly many now prefer to replace ‘‘system’’ with ‘‘science’’. Many of these technical issues, such as the most meaningful use of colour and texture in computer generated maps, may seem somewhat arcane to veterinary epidemiologists simply wanting to draw a map to highlight areas of enhanced disease risk. However, the issue that impacts immediately after the map is drawn is how to undertake a statistical analysis of the data, as current GIS software provides few effective and robust tools for this purpose. In part, this derives from quantitative users being a small and specialist subset of the GIS market, but equally it is a result of the complexity of applying statistics to spatial data. The underlying problem is that of ‘‘spatial autocorrelation’’, the tendency for measurements close in space to be close in value. This is one of the tantalising paradoxes of spatial data, as almost always it leads to a result in terms of maps that identify clusters of disease or areas of apparently elevated risk. However, once one realises that such a ‘‘result’’ is the norm, the challenge then becomes how to meaningfully analyse and interpret it. Thus, the questions become, is there truly an elevated disease risk, or do the clusters simply reflect the distribution of the population? Could the clusters represent sampling biases, such that all one is seeing on the map is areas where sampling was possible? These questions can only be answered by a robust study design and thorough statistical analysis. Nonetheless, when one looks for the statistical routines and advice on how to use them, the challenging nature of spatial analysis becomes apparent (Pfieffer, 2000). Fortunately, the challenge of how to undertake spatial analyses of veterinary epidemiological data is one that has been taken up with enthusiasm by workers in the past few years, as becomes apparent when comparing this Special Edition with the previous one (Durr and Pfieffer, 2002). In the first special issue, only one paper could be highlighted as introducing a new statistical methodology that by Christoph Staubach, who used Bayesian hierarchical modelling to overcome problems of sampling bias while investigating the prevalence of pseudorabies virus in wild boar in eastern Germany. This innovative paper was a harbinger of greater use of Bayesian methodologies, one that will be familiar to readers of Preventive Veterinary Medicine in other fields of epidemiology, particularly the analysis and interpretation of laboratory test results. Bayesian approaches to data analysis are of course not new, but the problem has always been how to practically implement them for anything other than trivial problems. The development of the software package WinBUGS in the 1990s under the enlightened sponsorship of the Medical Research Council of the UK has made it possible to solve previously intractable analyses. This is fully reflected in the papers collated here, with Bayesian approaches used for spatial survival analyses (Sanchez et al.), spatial regression (Durr et al.), spatio-temporal analysis (Clements et al.) epidemic disease modelling (Lawson and Zhou), and modelling spatial risk (Stevenson et al.), all except the last implemented using WinBUGS. Of course, this rush to all things Bayesian poses many new challenges, and novice practitioners need to be warned of the dangers of using uninformative priors and undertaking insufficient iterations. But, there is no doubt that this approach has transformed, and will continue to transform, the type of analyses possible in spatial epidemiology.
Preface / Preventive Veterinary Medicine 71 (2005) 137–140
139
One of the encouraging aspects of the GISVET’04 conference was that it bought together a wide community of scientists interested in the use of GIS and spatial analyses to animal health problems, including statisticians, wildlife ecologists and human and veterinary epidemiologists. This feature is somewhat more apparent in our accompanying conference proceedings (Durr and Martin, 2004), but is reflected here in the two explicitly modelling papers, those by Michael Ward and David Smith et al., who tackle issues of the spread of West Nile Virus Disease in horses and rabies in racoons, respectively. Mathematical modelling of disease has tendered to suffer from its complexity and thus opacity to all but specialist practitioners, and this has frequently engendered suspicion from more traditional epidemiologists. Displaying modelling predictions on maps has the potential to make their usefulness (or otherwise) more obvious, and this is an area in which we predict that GIS will act as a bridge helping to span the gulf between mathematical and conventional epidemiology. Many people helped to enable GISVET’04 to take place and thus make possible this Special Edition. In particular, we would like to express our gratitude to the organizing committee of the conference in Guelph; in particular, Bruce McNab of the Ontario Ministry of Agriculture & Food, Victoria Edge of the Public Health Agency of Canada, Muriel Burke of the University of Guelph and Vincent Adcock of the Veterinary Laboratories Agency. Without their efforts, the conference would never have achieved the high standard that it did. To the Dean of the Ontario Veterinary College, the Chair of Population Medicine, the Chair of the Department of Geography, and the Conference Services at the University of Guelph, we send our thanks for their assistance. We are also grateful to Dirk Pfeiffer, Angus Cameron, Graeme Garner, Rowland Tinline, Michael Ward, Christoph Staubach, and others (who we may have temporarily forgotten) for helping us to select the research papers presented here. We also acknowledge the assistance of the Editorial Office of Preventive Veterinary Medicine and its Editor-inChief, Dr. Hollis Erb, in helping us deliver what we truly believe to be a superb collection of scientific papers. Finally, we would like to thank three organizations that helped with financial support of the conference; these include the Ontario Ministry of Agriculture and Food, Habit (H.A.B.I.T. Research Ltd., 692 Sumas Street, Vic., BC, Canada V8T 4S6, [email protected]), and ESRI (ESRI Canada, 49 Gervais Drive, Toronto, Ont., Canada M3C 1Y9, http://www.esricanada.com/).
References Durr, P., Gatrell, A. (Eds.), 2004. GIS and Spatial Analysis in Veterinary Science. CABI, Wallinford, Oxon, 303 pp. Durr, P.A., Pfieffer, D.U. (Eds.), 2002. Spatial epidemiology. Research papers prepared for and presented at the GISVET conference, Lancaster University, England, September 10–14, 2001. Prev. Vet. Med. 56, 1 (special ed.), 103 pp. Pfieffer, D.U., 2000. Spatial analysis—a new challenge for veterinary epidemiologists. In: Proceedings of the Society for Veterinary Epidemiology and Preventive Medicine, University of Edinburgh, March 29–31, pp. 86–106.
140
Preface / Preventive Veterinary Medicine 71 (2005) 137–140
Durr, P.A., Martin, S.W. (Eds.), 2004. Proceedings of GISVET’04—Second International Conference on the Applications of GIS and Spatial Analysis to Veterinary Science, University of Guelph, June 23–25, VLA, Weybridge, England, 103 pp. http://www.gisvet.org/.
Peter Durr Veterinary Laboratories Agency, Weybridge, Surrey, England Wayne Martin* Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ont., Canada N1G 2W1 *Corresponding author. Tel.: +1 519 824 4120x54045; fax: +1 519 763 3117