Disease prediction models and operational readiness.

The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by e...

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Autores principales:	Courtney D Corley, Laura L Pullum, David M Hartley, Corey Benedum, Christine Noonan, Peter M Rabinowitz, Mary J Lancaster
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2d3ec7e8bd554a9090a415292d8edb38
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spelling	oai:doaj.org-article:2d3ec7e8bd554a9090a415292d8edb382021-11-18T08:27:14ZDisease prediction models and operational readiness.1932-620310.1371/journal.pone.0091989https://doaj.org/article/2d3ec7e8bd554a9090a415292d8edb382014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24647562/?tool=EBIhttps://doaj.org/toc/1932-6203The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.Courtney D CorleyLaura L PullumDavid M HartleyCorey BenedumChristine NoonanPeter M RabinowitzMary J LancasterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e91989 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Courtney D Corley Laura L Pullum David M Hartley Corey Benedum Christine Noonan Peter M Rabinowitz Mary J Lancaster Disease prediction models and operational readiness.
description	The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.
format	article
author	Courtney D Corley Laura L Pullum David M Hartley Corey Benedum Christine Noonan Peter M Rabinowitz Mary J Lancaster
author_facet	Courtney D Corley Laura L Pullum David M Hartley Corey Benedum Christine Noonan Peter M Rabinowitz Mary J Lancaster
author_sort	Courtney D Corley
title	Disease prediction models and operational readiness.
title_short	Disease prediction models and operational readiness.
title_full	Disease prediction models and operational readiness.
title_fullStr	Disease prediction models and operational readiness.
title_full_unstemmed	Disease prediction models and operational readiness.
title_sort	disease prediction models and operational readiness.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/2d3ec7e8bd554a9090a415292d8edb38
work_keys_str_mv	AT courtneydcorley diseasepredictionmodelsandoperationalreadiness AT lauralpullum diseasepredictionmodelsandoperationalreadiness AT davidmhartley diseasepredictionmodelsandoperationalreadiness AT coreybenedum diseasepredictionmodelsandoperationalreadiness AT christinenoonan diseasepredictionmodelsandoperationalreadiness AT petermrabinowitz diseasepredictionmodelsandoperationalreadiness AT maryjlancaster diseasepredictionmodelsandoperationalreadiness
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Disease prediction models and operational readiness.

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