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

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 sur...

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Autores principales: Cheng, Qu, Collender, Philip A., Heaney, Alexandra K., Li, Xintong, Dasan, Rohini, Li, Charles, Lewnard, Joseph A., Zelner, Jonathan L., Liang, Song, Chang, Howard H., Waller, Lance A., Lopman, Benjamin A., Yang, Changhong, Remais, Justin V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744064/
https://www.ncbi.nlm.nih.gov/pubmed/33275606
http://dx.doi.org/10.1371/journal.pcbi.1008477
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author Cheng, Qu
Collender, Philip A.
Heaney, Alexandra K.
Li, Xintong
Dasan, Rohini
Li, Charles
Lewnard, Joseph A.
Zelner, Jonathan L.
Liang, Song
Chang, Howard H.
Waller, Lance A.
Lopman, Benjamin A.
Yang, Changhong
Remais, Justin V.
author_facet Cheng, Qu
Collender, Philip A.
Heaney, Alexandra K.
Li, Xintong
Dasan, Rohini
Li, Charles
Lewnard, Joseph A.
Zelner, Jonathan L.
Liang, Song
Chang, Howard H.
Waller, Lance A.
Lopman, Benjamin A.
Yang, Changhong
Remais, Justin V.
author_sort Cheng, Qu
collection PubMed
description 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.
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spelling pubmed-77440642020-12-31 The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures Cheng, Qu Collender, Philip A. Heaney, Alexandra K. Li, Xintong Dasan, Rohini Li, Charles Lewnard, Joseph A. Zelner, Jonathan L. Liang, Song Chang, Howard H. Waller, Lance A. Lopman, Benjamin A. Yang, Changhong Remais, Justin V. PLoS Comput Biol Research Article 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. Public Library of Science 2020-12-04 /pmc/articles/PMC7744064/ /pubmed/33275606 http://dx.doi.org/10.1371/journal.pcbi.1008477 Text en © 2020 Cheng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cheng, Qu
Collender, Philip A.
Heaney, Alexandra K.
Li, Xintong
Dasan, Rohini
Li, Charles
Lewnard, Joseph A.
Zelner, Jonathan L.
Liang, Song
Chang, Howard H.
Waller, Lance A.
Lopman, Benjamin A.
Yang, Changhong
Remais, Justin V.
The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures
title The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures
title_full The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures
title_fullStr The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures
title_full_unstemmed The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures
title_short The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures
title_sort dios framework for optimizing infectious disease surveillance: numerical methods for simulation and multi-objective optimization of surveillance network architectures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744064/
https://www.ncbi.nlm.nih.gov/pubmed/33275606
http://dx.doi.org/10.1371/journal.pcbi.1008477
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