scholarly journals The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures

2020 ◽  
Vol 16 (12) ◽  
pp. e1008477
Author(s):  
Qu Cheng ◽  
Philip A. Collender ◽  
Alexandra K. Heaney ◽  
Xintong Li ◽  
Rohini Dasan ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Surveillance ◽  
Formal Analysis ◽  
Design Parameters ◽  
Evidence Based ◽  
Surveillance Systems ◽  
Network Architectures ◽  
Surveillance Network ◽  
Infectious Disease Surveillance ◽  
The Many

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 an 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—the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework—for the identification of optimal surveillance designs through mathematical representations of disease and surveillance processes, definition of objective functions, and 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.

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

2020 ◽  
Author(s):  
Qu Cheng ◽  
Philip A. Collender ◽  
Alexandra K. Heaney ◽  
Xintong Li ◽  
Rohini Dasan ◽  
...  
Keyword(s):  
System Design ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Design Parameters ◽  
Surveillance Systems ◽  
Case Definitions ◽  
Surveillance Network ◽  
Infectious Disease Surveillance ◽  
The Many ◽  
Operational Constraints

AbstractInfectious 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.Author summaryDisease surveillance systems are essential for understanding the epidemiology of infectious diseases and improving population health. A well-designed surveillance system can achieve a high level of fidelity in estimates of interest (e.g., disease trends, risk factors) within its operational constraints. Currently, design parameters that define surveillance systems (e.g., number and placement of the surveillance sites, target populations, case definitions) are selected largely by expert opinion and practical considerations. Such an informal approach is less tenable when multiple aspects of surveillance design—or multiple surveillance objectives— need to be considered simultaneously, and are subject to resource or logistical constraints. Here we propose a framework to optimize surveillance system design given a set of defined surveillance objectives and a dynamical model of the disease system under study. The framework provides a platform to conduct in silico surveillance system design, and allows the formulation of surveillance guidelines based on quantitative evidence, tailored to local realities and priorities. The approach facilitates greater collaboration between health planners and computational and data scientists to advance surveillance science and strengthen the architecture of surveillance networks.

scholarly journals New eyes: improving Europe’s infectious disease surveillance

2007 ◽  
Vol 12 (5) ◽  
pp. 1-2
Author(s):  
D O’Flanagan
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Disease Surveillance ◽  
Public Health Intervention ◽  
Health Intervention ◽  
Sufficient Information ◽  
Surveillance Systems ◽  
Infectious Disease Surveillance ◽  
Microbiological Data

This edition of Eurosurveillance contains reports of infectious disease surveillance systems from all corners of Europe. In some instances, routinely collected notifiable data coupled with microbiological data can provide sufficient information to allow appropriate public health intervention.

scholarly journals Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review

2019 ◽  
Vol 30 (4) ◽  
pp. 639-647 ◽  
Author(s):  
Janneke W Duijster ◽  
Simone D A Doreleijers ◽  
Eva Pilot ◽  
Wim van der Hoek ◽  
Geert Jan Kommer ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Scoping Review ◽  
Disease Surveillance ◽  
Syndromic Surveillance ◽  
Surveillance Systems ◽  
Call Centre ◽  
Infectious Disease Surveillance ◽  
Respiratory Illnesses ◽  
Key Informants

Abstract Background Syndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking. Methods A scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants. Results Forty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data. Conclusions Various dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.

scholarly journals Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk

2019 ◽  
Vol 10 (1) ◽  
pp. 94-115
Author(s):  
Stephen L ROBERTS
Keyword(s):  
Infectious Disease ◽  
Big Data ◽  
Disease Surveillance ◽  
Disease Outbreaks ◽  
Surveillance Systems ◽  
Infectious Disease Surveillance ◽  
Novel Technologies ◽  
Infectious Disease Outbreaks ◽  
The Government ◽  
Processing Techniques

This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic.

scholarly journals Comparing national infectious disease surveillance systems: China and the Netherlands

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Willemijn L. Vlieg ◽  
Ewout B. Fanoy ◽  
Liselotte van Asten ◽  
Xiaobo Liu ◽  
Jun Yang ◽  
...  
Keyword(s):  
Infectious Disease ◽  
The Netherlands ◽  
Disease Surveillance ◽  
Surveillance Systems ◽  
Infectious Disease Surveillance
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