Challenges in designing a disease surveillance plan: What we have and what we need?

Sparks, Ross

doi:10.1080/19488300.2013.820241

Cited by 10 publications

(6 citation statements)

References 106 publications

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“…Jordan and Benneyan27 and Sparks28 have recently provided thorough reviews of the limitations of control chart use in healthcare, and some common interpretation errors. Although those authors generally focused on disease surveillance, their discussions can easily be extended to injury monitoring.…”

Section: Discussionmentioning

confidence: 99%

Statistical process control charts for monitoring military injuries

2017

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Section: Discussionmentioning

confidence: 99%

Statistical process control charts for monitoring military injuries

2017

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“…Sonesson and Bock [47] gave an excellent review of prospective public health surveillance, but much work has been done since. An updated review was given by Sparks [48]. In addition, Tsui et al [52] gave a review of temporal and spatiotemporal surveillance methods for disease surveillance in public health.…”

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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“…Lazarus (2010) . A large body of literature focuses on epidemiological detection problems, since many infectious diseases undergo considerable seasonal fluctuations with peaks seriously impacting the healthcare systems ( Sparks, 2013 , Unkel et al, 2012 ). National surveillance systems are implemented world-wide to rapidly detect outbreaks of influenza-like illnesses, and assess the effectiveness of influenza vaccines ( Thompson et al, 2006 , Vega et al, 2015 ).…”

Section: Monitor– Estimation Of the State Of A Pandemicmentioning

confidence: 99%

Data-driven methods for present and future pandemics: Monitoring, modelling and managing

Álamo

Reina

Millán

et al. 2021

Annual Reviews in Control

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This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.

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Challenges in designing a disease surveillance plan: What we have and what we need?

Cited by 10 publications

References 106 publications

Statistical process control charts for monitoring military injuries

Statistical process control charts for monitoring military injuries

Control Phase

Data-driven methods for present and future pandemics: Monitoring, modelling and managing

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