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

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 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.

Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections

“…We demonstrated that, with the same level of typing effort as the existing network, optimal designs chosen using DIOS can reduce the mean absolute error of estimates of serotype- PLOS COMPUTATIONAL BIOLOGY specific incidence rates and proportions of clinical cases caused by each serotype by 14.1 and 13.3 percent, respectively. Although beyond the scope of this study, the DIOS framework accommodates multi-objective optimization as well [20], providing a means to identify optimal designs for simultaneously optimizing both objectives. Changes to the total number of cases sampled for subtyping minimally impacted the relative performance of surveillance designs.…”

“…Simulation framework. DIOS [20] is a simulation-based optimization framework to facilitate the design of robust disease surveillance systems. DIOS functions by linking disease system models that simulate epidemiologic processes with surveillance system models that simulate information derived from alternative surveillance system designs.…”

“…The disease system model (see [20]) may be statistical, mechanistic, or an ensemble of different models or parameters that account for epistemic and parametric uncertainties, and should be developed with special attention to representing any processes thought to be relevant to the surveillance process. Multiple realizations of the disease system model, which may comprise incident cases or other phenomena of interest, is then filtered through measurement processes simulated by the surveillance model [20], which mimics relevant data collection and processing behaviors of a surveillance system, subsequently yielding estimates of the target epidemiologic parameter(s) (e.g., disease incidence; probability of an outbreak; change in incidence following intervention) that can be compared to true underlying values generated by the disease system model to assess the performance of the surveillance design.…”

“…subtypes, evaluation of the effectiveness of subtype-specific interventions, and confirmation of the elimination or eradication of a specific subtype. Multiple objectives can also be evaluated simultaneously through multi-objective optimization, as we have demonstrated elsewhere [20].…”

“…Here, we develop methods to support the optimization of sampling clinical cases for laboratory typing with the goal of improved monitoring of the distribution of specific pathogen subtypes, while abiding by constraints on available resources, e.g., the total number of clinical cases subjected to subtyping. Our work is based on the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework [20], which iteratively evaluates surveillance network performance on predefined goals while varying surveillance system design parameters using numerical optimization algorithms. We adapt the DIOS framework to the problem of optimal allocation of laboratory typing resources across subregions and case severity groups of a surveillance network in order to minimize error in estimating the incidence rates of pathogen subtypes causing a clinically-diagnosed disease.…”