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

Abstract

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 ...