Outbreaks and Investigations

Dato, Virginia

doi:10.1016/b978-012369378-5/50004-1

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Disease Surveillance as defined by the Centers for Disease Control and Prevention (CDC) [8] is the ongoing, systematic collection, analysis, interpretation, and dissemination of data about a health-related event for use in public health action to reduce morbidity and mortality and to improve wellbeing. It is a complex process that uses data from a plethora of sources and distributes its activities over several geographically dispersed actors with heterogeneous profiles [6,12,44,3]. Each actor plays a specific role in the system and offers a number of services needed by other stakeholders.…”

Section: A Disease Surveillance Systemmentioning

confidence: 99%

Active workspaces

Badouel

Hélouët

Kouamou³

et al. 2015

SIGAPP Appl. Comput. Rev.

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This paper presents a purely declarative approach to artifactcentric collaborative systems, a model which we introduce in two stages. First, we assume that the workspace of a user is given by a mindmap, shortened to a map, which is a tree used to visualize and organize tasks in which he or she is involved, with the information used for the resolution of these tasks. We introduce a model of guarded attribute grammar, or GAG, to help the automation of updating such a map. A GAG consists of an underlying grammar, that specifies the logical structure of the map, with semantic rules which are used both to govern the evolution of the tree structure (how an open node may be refined to a subtree) and to compute the value of some of its attributes (which derives from contextual information). The map enriched with this extra information is termed an active workspace. Second, we define collaborative systems by making the various user's active workspaces communicate with each other. The communication uses message passing without shared memory thus enabling convenient distribution on an asynchronous architecture. We present some formal properties of the model of guarded attribute grammars, then a language for their specification and we illustrate the approach on a case study for a disease surveillance system.

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Section: A Disease Surveillance Systemmentioning

confidence: 99%

Active workspaces

Badouel

Hélouët

Kouamou³

et al. 2015

SIGAPP Appl. Comput. Rev.

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“…This overlapping disease activity confounds diagnosis and treatment of patients presenting with influenza-like illnesses (ILIs) and the associated high caseloads stress the clinical and logistical capacity of the healthcare system. Thus, accurately detecting and tracking overlapping outbreaks due to these viruses is an important task with public health implications and clinical repercussions for those at high risk [1,2,3,4,5]. The ideal surveillance system will notice an outbreak after just a few cases that may be distributed across several shifts at multiple hospitals.…”

Section: Introduction Backgroundmentioning

confidence: 99%

A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation

Aronis,

Ye,

Espino

et al. 2024

JMIR Public Health Surveill

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Background:The early identification of outbreaks of both known and novel influenza-like illnesses is an important public health problem. Objective:The design and testing of a tool that detects and tracks outbreaks of both known and novel influenza-like illness, such as COVID-19, accurately and early.Methods: This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known influenza-like illnesses in a hospital emergency department using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease which may represent a novel disease outbreak. Results:We include results based on modeling the diseases influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for five emergency departments in Allegheny County Pennsylvania from June 1, 2010 through May 31, 2015. We also include the results of detecting the likely outbreak of an unmodeled disease during the mentioned time period, which in retrospect was very likely an outbreak of Enterovirus D68. Conclusions:The results reported in this paper provide support that ILI Tracker was able to track well four modeled ILI-like diseases over a one-year period, relative to laboratory confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014, as well as clinically characterize that outbreak disease accurately.

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“…This overlapping disease activity confounds diagnosis and treatment of patients presenting with influenza-like illnesses and the associated high caseloads stress the clinical and logistical capacity of the healthcare system. Thus, accurately detecting and tracking overlapping outbreaks due to these viruses is an important task with public health implications and clinical repercussions for those at high risk [1,2,3,4,5]. The ideal surveillance system will notice an outbreak after just a few cases that may be distributed across several shifts at multiple hospitals.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian System to Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases

Aronis

Espino

et al. 2023

Preprint

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It would be highly desirable to have a tool that detects the outbreak of a new influenza-like illness, such as COVID-19, accurately and early. This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known influenza-like illnesses in a hospital emergency department using findings extracted from patient-care reports using natural language processing. We include results based on modeling the diseases influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for five emergency departments in Allegheny County Pennsylvania from June 1, 2010 through May 31, 2015. We then show how the algorithm can be extended to detect the presence of an unmodeled disease which may represent a novel disease outbreak. We also include results for detecting an outbreak of an unmodeled disease during the mentioned time period, which in retrospect was very likely an outbreak of Enterovirus D68.

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Outbreaks and Investigations

Cited by 6 publications

References 24 publications

Active workspaces

Active workspaces

A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation

A Bayesian System to Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases

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