A review of some methods for analysis of space-time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, in practice, a variety of factors determine what methods are appropriate for surveillance. In this review, we focus on the role of contextual factors such as scale, scope, surveillance objective, disease characteristics, and technical issues in relation to commonly used approaches to surveillance. Methods are classified as testing-based or model-based approaches. Reviewing methods in the context of factors other than algorithm performance highlights important aspects of implementing and selecting appropriate disease surveillance methods.

Because many infectious diseases are emerging in animals in low-income and middle-income countries, surveillance of animal health in these areas may be needed for forecasting disease risks to humans. We present an overview of a mobile phone–based frontline surveillance system developed and implemented in Sri Lanka. Field veterinarians reported animal health information by using mobile phones. Submissions increased steadily over 9 months, with ≈4,000 interactions between field veterinarians and reports on the animal population received by the system. Development of human resources and increased communication between local stakeholders (groups and persons whose actions are affected by emerging infectious diseases and animal health) were instrumental for successful implementation. The primary lesson learned was that mobile phone–based surveillance of animal populations is acceptable and feasible in lower-resource settings. However, any system implementation plan must consider the time needed to garner support for novel surveillance methods among users and stakeholders.

Aim The spatial extent of western Canada's current epidemic of mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae, Scolytinae), is increasing. The roles of the various dispersal processes acting as drivers of range expansion are poorly understood for most species. The aim of this paper is to characterize the movement patterns of the mountain pine beetle in areas where range expansion is occurring, in order to describe the fine-scale spatial dynamics of processes associated with mountain pine beetle range expansion.Location Three regions of Canada's Rocky Mountains: Kicking Horse Pass, Yellowhead Pass and Pine Pass.Methods Data on locations of mountain pine beetle-attacked trees of predominantly lodgepole pine (Pinus contorta var. latifolia) were obtained from annual fixed-wing aircraft surveys of forest health and helicopter-based GPS surveys of mountain pine beetle-damaged areas in British Columbia and Alberta. The annual (1999)(2000)(2001)(2002)(2003)(2004)(2005) spatial extents of outbreak ranges were delineated from these data. Spatial analysis was conducted using the spatial-temporal analysis of moving polygons (STAMP), a recently developed pattern-based approach. ResultsWe found that distant dispersal patterns (spot infestations) were most often associated with marginal increases in the areal size of mountain pine beetle range polygons. When the mountain pine beetle range size increased rapidly relative to the years examined, local dispersal patterns (adjacent infestation) were more common. In Pine Pass, long-range dispersal (> 2 km) markedly extended the north-east border of the mountain pine beetle range. In Yellowhead Pass and Kicking Horse Pass, the extension of the range occurred incrementally via ground-based spread.Main conclusions Dispersal of mountain pine beetle varies with geography as well as with host and beetle population dynamics. Although colonization is mediated by habitat connectivity, during periods of low overall habitat expansion, dispersal to new distant locations is common, whereas during periods of rapid invasion, locally connected spread is the dominant mode of dispersal. The propensity for long-range transport to establish new beetle populations, and thus to be considered a driver of range expansion, is likely to be determined by regional weather patterns, and influenced by local topography. We conclude that STAMP appears to be a useful approach for examining changes in biogeograpical ranges, with the potential to reveal both fine-and large-scale patterns.

Disease surveillance makes use of information technology at almost every stage of the process, from data collection and collation, through to analysis and dissemination. Automated data collection systems enable near-real time analysis of incoming data. This context places a heavy burden on software used for space-time surveillance. In this paper, we review software programs capable of space-time disease surveillance analysis, and outline some of their salient features, shortcomings, and usability. Programs with space-time methods were selected for inclusion, limiting our review to ClusterSeer, SaTScan, GeoSurveillance and the Surveillance package for R. We structure the review around stages of analysis: preprocessing, analysis, technical issues, and output. Simulated data were used to review each of the software packages. SaTScan was found to be the best equipped package for use in an automated surveillance system. ClusterSeer is more suited to data exploration, and learning about the different methods of statistical surveillance.

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