03376nas a2200397   4500000000100000008004100001260003700042653001200079653002900091653004100120653001300161653004900174653002200223653004000245100001200285700001700297700001400314700000900328700001200337700000900349700001500358700001400373700001200387700001300399700001400412700001400426700001100440700001400451700001600465245017500481856010300656300001300759490000700772520218500779022001402964       2020                            d  bPublic Library of Science (PLoS)10aEcology10aModelling and Simulation10aComputational Theory and Mathematics10aGenetics10aEcology, Evolution, Behavior and Systematics10aMolecular Biology10aCellular and Molecular Neuroscience1 aCheng Q1 aCollender PA1 aHeaney AK1 aLi X1 aDasan R1 aLi C1 aLewnard JA1 aZelner JL1 aLiang S1 aChang HH1 aWaller LA1 aLopman BA1 aYang C1 aRemais JV1 aAlthouse BM00aThe DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures  uhttps://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008477&type=printable  ae10084770 v163 a<jats:p>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.</jats:p>  a1553-7358