Bayesian adaptive algorithms for locating HIV mobile testing services

Gonsalves, Gregg; Copple, J. Tyler; Johnson, Terri L.; Paltiel, A. David; Warren, Joshua L.

doi:10.1186/s12916-018-1129-0

Cited by 9 publications

(12 citation statements)

References 20 publications

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“…Recent research has provided some initial examples of quantitative infectious disease surveillance design optimization [ 23 , 24 ]. In one study, researchers estimated that an optimal relocation of Iowa’s existing 22 ILINet sentinel sites could increase population coverage of the network from 56% to 75% [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…The absence of objective criteria and methods to evaluate and iteratively reconfigure surveillance system design can lead to inefficient use of limited resources. For example, in China, current requirements specify that 5-15 influenza-like illness (ILI) cases are required to be sampled per week at each of the 556 influenza sentinel hospitals for laboratory confirmation [18]. If the total sample size is fixed, it may be that reducing the number of sentinel sites (e.g., prioritizing sites in populous regions and with high levels of population movement), while increasing the sample sizes at the remaining sites, could yield more timely detection of outbreaks with the same level of resources.…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has provided some early examples of quantitative infectious disease surveillance design optimization [19, 20]. In one study, researchers estimated that an optimal relocation of Iowa’s existing 22 ILINet sentinel sites could increase population coverage of the network from 56% to 75% [21].…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Collender

Heaney

et al. 2020

Preprint

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29Infectious disease surveillance systems provide vital data for guiding disease prevention and 30 control policies, yet the formalization of methods to optimize surveillance networks has largely 31 been overlooked. Decisions surrounding surveillance design parameters-such as the number 32 and placement of surveillance sites, target populations, and case definitions-are often 33 determined by expert opinion or deference to operational considerations, without formal analysis 34 of the influence of design parameters on surveillance objectives. Here we propose a simulation 35 framework to guide evidence-based surveillance network design to better achieve specific 36 surveillance goals with limited resources. We define evidence-based surveillance design as a 37constrained, multi-dimensional, multi-objective, dynamic optimization problem, acknowledging 38 the many operational constraints under which surveillance systems operate, the many 39 dimensions of surveillance system design, the multiple and competing goals of surveillance, and 40 the complex and dynamic nature of disease systems. We describe an analytical framework for 41 the identification of optimal designs through mathematical representations of disease and 42 surveillance processes, definition of objective functions, and the approach to numerical 43 optimization. We then apply the framework to the problem of selecting candidate sites to expand 44 an existing surveillance network under alternative objectives of: (1) improving spatial prediction 45of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor 46 on disease. Results of this demonstration illustrate how optimal designs are sensitive to both 47 surveillance goals and the underlying spatial pattern of the target disease. The findings affirm 48 the value of designing surveillance systems through quantitative and adaptive analysis of 49 network characteristics and performance. The framework can be applied to the design of 50 surveillance systems tailored to setting-specific disease transmission dynamics and surveillance 51 needs, and can yield improved understanding of tradeoffs between network architectures. 52 53Author summary 54 Disease surveillance systems are essential for understanding the epidemiology of 55 infectious diseases and improving population health. A well-designed surveillance system can 56 achieve a high level of fidelity in estimates of interest (e.g., disease trends, risk factors) within its 57 operational constraints. Currently, design parameters that define surveillance systems (e.g., 58number and placement of the surveillance sites, target populations, case definitions) are 59 selected largely by expert opinion and practical considerations. Such an informal approach is 60 less tenable when multiple aspects of surveillance design-or multiple surveillance objectives-61 need to be considered simultaneously, and are subject to resource or logistical constraints. 62Here we propose a framework to optimize surveillance system design given a set ...

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“…These contributions will combine detailed spatial statistical methods and spatial dynamic models together with spatially resolved sociodemographic, environmental, epidemiological, and/or genetic data to disentangle the collective dynamics of infectious disease transmission to guide public health policy [ 5 , 6 ]. Additionally, it will examine the spatial distribution of infectious disease burden, including the identification of hotspots and case clustering [ 6 – 9 ], calibrate dynamic models for forecasting the trajectory of epidemics [ 5 , 6 , 9 ], and simulate scenario analyses to evaluate the impact of different control strategies on epidemic control at various spatial scales [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial infectious disease epidemiology: on the cusp

Chowell

Rothenberg

2018

BMC Med

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Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.

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Bayesian adaptive algorithms for locating HIV mobile testing services

Cited by 9 publications

References 20 publications

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

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

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

Spatial infectious disease epidemiology: on the cusp

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