Review of methods for space–time disease surveillance

Robertson, Colin; Nelson, Trisalyn A.; MacNab, Ying C.; Lawson, Andrew

doi:10.1016/j.sste.2009.12.001

Cited by 125 publications

(114 citation statements)

References 82 publications

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“…Appropriate spatial and temporal units should be used to calculate and present VBD incidence, since too crude a scale of data collection or analysis may obscure fine-scale risk patterns (58,59,60). Surveillance systems are increasingly linked to automated data analysis aimed at detecting unusual events and spatiotemporal clustering of events (space-time interaction), for rapid notification to decision-makers.…”

Section: Participatory Surveillancementioning

confidence: 99%

“…Surveillance systems are increasingly linked to automated data analysis aimed at detecting unusual events and spatiotemporal clustering of events (space-time interaction), for rapid notification to decision-makers. A problem with such methods in many regions is a lack of knowledge of the underlying population at risk and therefore the specification of the baseline disease risk; developing methods to account for spatial heterogeneity in background rates, as well as for movements in the population at risk, is a continuing area of research (59).…”

Section: Participatory Surveillancementioning

confidence: 99%

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Epidemiological surveillance methods for vector-borne diseases

Thompson¹,

Etter²

2015

Rev. Sci. Tech. OIE

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SummaryCompared with many other diseases, the ever-increasing threat of vectorborne diseases (VBDs) represents a great challenge to public and animal health managers. Complex life cycles, changing distribution ranges, a variety of potential vectors and hosts, and the possible role of reservoirs make surveillance for VBDs a grave concern in a changing environment with increasing economic constraints. Surveillance activities may have various specific objectives and may focus on clinical disease, pathogens, vectors, hosts and/or reservoirs, but ultimately such activities should improve our ability to predict, prevent and/or control the diseases concerned. This paper briefly reviews existing and newly developed tools for the surveillance of VBDs. A range of examples, by no means exhaustive, illustrates that VBD surveillance usually involves a combination of methods to achieve its aims, and is best accomplished when these techniques are adapted to the specific environment and constraints of the region. More so than any other diseases, VBDs respect no administrative boundaries; in addition, animal, human and commodity movements are increasing dramatically, with illegal or unknown movements difficult to quantify. Vector-borne disease surveillance therefore becomes a serious issue for local and national organisations and is being conducted more and more at the regional and international level through multidisciplinary networks. With economic and logistical constraints, tools for optimising and evaluating the performance of surveillance systems are essential and examples of recent developments in this area are included. The continuous development of mapping, analytical and modelling tools provides us with an enhanced ability to interpret, visualise and communicate surveillance results. This review also demonstrates the importance of the link between surveillance and research, with interactions and benefits in both directions.

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Section: Participatory Surveillancementioning

confidence: 99%

Section: Participatory Surveillancementioning

confidence: 99%

Epidemiological surveillance methods for vector-borne diseases

Thompson¹,

Etter²

2015

Rev. Sci. Tech. OIE

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“…Sometimes, the exact location is known, but most often, one only knows in which of several subregions the events are located. Most of the work on prospective spatiotemporal monitoring has been done in public health cluster detection applications, where the goal is to detect emerging clusters where disease rates are higher than expected [see, for example, the books by Lawson and Kleinman [25] and Rogerson and Yamada [45], and the review paper by Robertson et al [44].…”

Section: Role Of Process Monitoringmentioning

confidence: 99%

Control Phase

Muralidharan

2015

Six Sigma for Organizational Excellence

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• Document a new or improved process and measurement • Use statistical process control (SPC) for validating collection systems • Use SPC to ascertain the repeatability and reproducibility of metrics in an operational environment • Define the control plan and its supporting plans such as communications plan of the improvements and operational changes to the customers and stakeholders, prepare implementation and risk management plan, consolidate cost-benefit, and change management plan • Establish tracking procedures in an operational environment: monitor implementation, validate and stabilize performance gains, and jointly audit the results and confirm the financials • Setup control plans for tolerances, controls, measures, and standard operating procedures • Validate in-control process and benefits for process capability, MSA and Gage R&R, and documentation • Document and the follow-up steps • Exit review Control PlansSo far, our attempt has been to identify the most important contributing variables of the process. Through the completed five phases, we have been trying to establish the best process model free of all kinds of variations. The method so far enabled us to identify a viable model and helped us to quantify the nature of the relationship between the important variables of the process output. The statistical process control technique can now be employed with considerable effectiveness for monitoring and surveillance of the process. Techniques such as control charts and capability assessment can be used to monitor the process output and detect when changes in the inputs are required to bring the process back to an in-control state.

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“…Example applications include research in areas of ocean sea surface temperature (Birant andKut 2006, 2007), precipitation (Abraham and Tan 2009;Wu, Liu, and Chawla 2010), epidemiology (Buckeridge 2007;Robertson et al 2010), traffic (Shekhar, Lu, and Zhang 2003), crime (Aggarwal and Yu 2001;Lin and Brown 2006;Rogers, Barbara, and Domeiconi 2009), and terrorism (Gao et al 2013). Spatiotemporal outlier detection aims to find spatial outliers; however, instead of just looking at a single snapshot in time, it considers the behavior of these outliers over several time periods.…”

mentioning

confidence: 99%