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

Fig 1

Schematic of the DIOS framework.

The surveillance system optimization procedure uses data and knowledge about disease transmission and case ascertainment to identify optimal surveillance designs with regard to predefined surveillance goals. First, a disease system model is defined, using observed epidemiologic data and/or theory, and taking into account relevant factors influencing disease dynamics or distribution. Multiple realizations of disease data () may be generated to explore optimal designs under uncertainty or variability of the underlying system (see Specify and parameterize disease system model). Furthermore, an ensemble of disease models can be combined to reduce the chance of model misspecification. Next, a surveillance model is defined to represent how information on the state of the disease system is captured as a function of design parameters θ and any other relevant variables (e.g., factors known to affect the sensitivity and specificity of a diagnostic test, or estimated underreporting rates for an area; see Specify and parameterize surveillance model). To initiate the optimization process, an initial design parameter set, θ₁, is drawn from the design space subject to operational constraints g(θ_i) ≤ 0, h(θ_i) = 0 and, along with underlying disease data , input to the surveillance model to generate a realization of surveillance information, . The objective function, f, is evaluated based on the disease data , and surveillance information (see Define objective function(s)). If a stopping criterion (e.g., reaching a large number of iterations; de minimis improvement in objective function) is not met, a new design parameter set, θ_i, is proposed from the design space using metaheuristic search algorithms (e.g., simulated annealing, genetic algorithm, particle swarm algorithm) when the design space is large, or enumeration when the design space is small. This new design parameter set is then used to generate a new realization of surveillance information and evaluation of the objective functions (see Simulation optimization search). After a stopping criterion is met, design parameter sets with the best objective function values are output as optimal surveillance designs.

doi: https://doi.org/10.1371/journal.pcbi.1008477.g001