School closure as an influenza mitigation strategy: how variations in legal authority and plan criteria can alter the impact

Potter, Margaret A.; Brown, Shawn T.; Cooley, Phillip; Sweeney, Patricia M.; Hershey, Tina Batra; Gleason, Sherrianne M.; Lee, Bruce Y.; Keane, Christopher; Grefenstette, John J.; Burke, Donald S.

doi:10.1186/1471-2458-12-977

Cited by 22 publications

(20 citation statements)

References 29 publications

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“…68 Influenza outbreaks 69 and spates of other infectious diseases 70 have also received attention, with the method's flexibility meaning that investigations into future disease prevention scenarios have been possible. 69 Despite still being rare within the field, agent-based models have great potential within dentistry. 71 There have been a number of applications within the oral health literature, with some of this work taking on a geographical dimension.…”

Section: Advantages and Limitations Of Spatial Microsimulation Modementioning

confidence: 99%

Application of geographic information systems and simulation modelling to dental public health: Where next?

Broomhead

Ballas

Baker

2018

Comm Dent Oral Epid

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Public health research in dentistry has used geographic information systems since the 1960s. Since then, the methods used in the field have matured, moving beyond simple spatial associations to the use of complex spatial statistics and, on occasions, simulation modelling. Many analyses are often descriptive in nature; however, and the use of more advanced spatial simulation methods within dental public health remains rare, despite the potential they offer the field. This review introduces a new approach to geographical analysis of oral health outcomes in neighbourhoods and small area geographies through two novel simulation methods—spatial microsimulation and agent‐based modelling. Spatial microsimulation is a population synthesis technique, used to combine survey data with Census population totals to create representative individual‐level population datasets, allowing for the use of individual‐level data previously unavailable at small spatial scales. Agent‐based models are computer simulations capable of capturing interactions and feedback mechanisms, both of which are key to understanding health outcomes. Due to these dynamic and interactive processes, the method has an advantage over traditional statistical techniques such as regression analysis, which often isolate elements from each other when testing for statistical significance. This article discusses the current state of spatial analysis within the dental public health field, before reviewing each of the methods, their applications, as well as their advantages and limitations. Directions and topics for future research are also discussed, before addressing the potential to combine the two methods in order to further utilize their advantages. Overall, this review highlights the promise these methods offer, not just for making methodological advances, but also for adding to our ability to test and better understand theoretical concepts and pathways.

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Section: Advantages and Limitations Of Spatial Microsimulation Modementioning

confidence: 99%

Application of geographic information systems and simulation modelling to dental public health: Where next?

Broomhead

Ballas

Baker

2018

Comm Dent Oral Epid

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“…Collectively, we call these activities the “meta-model” (Figure 2). Alternative terms for this concept include the “integrated information supply chain,” or the “information ecosystem.” Public health meta-modeling activities include data acquisition, data curation, statistical inference and pattern detection, model development, representation of human behavior, consideration of pragmatics such as logistics 7 and legal structures, 8 cost-benefit analyses, visualization, and communication of model results.…”

Section: Not Just Model the Whole Meta-modelmentioning

confidence: 99%

Toward an Integrated Meta-model of Public Health Dynamics for Preparedness Decision Support

Burke

Grefenstette

2013

Journal of Public Health Management and Practice

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“…All of the language-based legal and policy sources are subject to interpretation, which are typically expressed not as numerical values but as categories. For example, Hodge et al 12 classified school closure laws on the basis of which governmental departments had decision-making authority, and Potter et al 13 compared such laws on the basis of whether or not statutes delegated centralized versus local authority and whether plans specified closure and reopening criteria. Economic resources are measured and reported as continuous values or amounts, such as dollars, full-time equivalent employees, square footage of space, and quantity of supplies.…”

Section: Data Requirements and Sourcesmentioning

confidence: 99%

Data for Preparedness Metrics

Potter

Houck

Miner

et al. 2013

Journal of Public Health Management and Practice

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Tracking progress toward the goal of preparedness for public health emergencies requires a foundation in evidence derived both from scientific inquiry and from preparedness officials and professionals. Proposed in this article is a conceptual model for this task from the perspective of the Centers for Disease Control and Prevention–funded Preparedness and Emergency Response Research Centers. The necessary data capture the areas of responsibility of not only preparedness professionals but also legislative and executive branch officials. It meets the criteria of geographic specificity, availability in standardized and reliable measures, parameterization as quantitative values or qualitative distinction, and content validity. The technical challenges inherent in preparedness tracking are best resolved through consultation with the jurisdictions and communities whose preparedness is at issue.

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School closure as an influenza mitigation strategy: how variations in legal authority and plan criteria can alter the impact

Cited by 22 publications

References 29 publications

Application of geographic information systems and simulation modelling to dental public health: Where next?

Application of geographic information systems and simulation modelling to dental public health: Where next?

Toward an Integrated Meta-model of Public Health Dynamics for Preparedness Decision Support

Data for Preparedness Metrics

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