About: Towards a simulation framework for optimizing infectious disease surveillance: An information theoretic approach for surveillance system design

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Towards a simulation framework for optimizing infectious disease surveillance: An information theoretic approach for surveillance system design
Creator	Liang, Song Waller, Lance Zelner, Jon Chang, Howard Cheng, Qu Yang, Changhong Lopman, Benjamin Remais, Justin Lewnard, Joseph Charles, Collender, Philip Dasan, Rohini Heaney, Alexandra Li, Xintong
topic	covid:57d42952a44eb0796f888a054af7c73ba71763ff#this
Source	MedRxiv
abstract	Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters, such as the number and placement of surveillance sites, target populations, and case definitions, are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources. We define evidence-based surveillance design as a constrained, multi-dimensional, multi-objective, dynamic optimization problem, acknowledging the many operational constraints under which surveillance systems operate, the many dimensions of surveillance system design, the multiple and competing goals of surveillance, and the complex and dynamic nature of disease systems. We describe an analytical framework for the identification of optimal designs through mathematical representations of disease and surveillance processes, definition of objective functions, and the approach to numerical optimization. We then apply the framework to the problem of selecting candidate sites to expand an existing surveillance network under alternative objectives of: (1) improving spatial prediction of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor on disease. Results of this demonstration illustrate how optimal designs are sensitive to both surveillance goals and the underlying spatial pattern of the target disease. The findings affirm the value of designing surveillance systems through quantitative and adaptive analysis of network characteristics and performance. The framework can be applied to the design of surveillance systems tailored to setting-specific disease transmission dynamics and surveillance needs, and can yield improved understanding of tradeoffs between network architectures.
has issue date	2020-04-10(xsd:dateTime)
bibo:doi	10.1101/2020.04.06.20048231
has license	medrxiv
sha1sum (hex)	57d42952a44eb0796f888a054af7c73ba71763ff
schema:url	https://doi.org/10.1101/2020.04.06.20048231
resource representing a document's title	Towards a simulation framework for optimizing infectious disease surveillance: An information theoretic approach for surveillance system design
resource representing a document's body	covid:57d42952a44eb0796f888a054af7c73ba71763ff#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/57d42952a44eb0796f888a054af7c73ba71763ff
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