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

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.Author summary: Laboratory-based tests can determine the specific agents that cause infectious diseases, providing important information for disease surveillance, and helping to understand the transmissibility, clinical spectrum, evolutionary trends, and subtype-specific risk factors of infections caused by pathogens with multiple types. However, pathogen typing is relatively expensive and scarce, and thus there is widespread interest in the optimal allocation of laboratory typing resources in the design of disease surveillance systems, even as such surveillance optimization methods have been understudied. Here we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to the problem of optimal allocation of laboratory-typing within clinical surveillance systems. We develop methods for optimizing allocation of laboratory-typing across locations and clinical subgroups (e.g., severe vs. mild cases), and demonstrate the approach using real-world data from a surveillance network monitoring Hand Foot and Mouth Disease in western China. Using a series of simulation-optimization studies, we identified surveillance networks that are capable of reducing the mean absolute error of serotype-specific incidence rates by 13.3% among severe cases, and 14.1% among all cases. The methods demonstrated here are but one of many approaches through which the DIOS framework could be utilized to better leverage laboratory-typing infrastructure to track pathogen-specific epidemiologic trends.