Key result
Applying the DIOS framework to optimize hand foot and mouth disease surveillance networks reduced the mean absolute error in estimated serotype-specific incidence rates by 14.1%.
Why the study?
Pathogen typing volume is often limited by cost and logistics, creating a need to optimize protocols for selecting clinical isolates to infer the frequency and distribution of pathogen subtypes.
Applying the DIOS framework to optimize surveillance networks can significantly improve the estimation of serotype-specific incidence rates without requiring additional laboratory resources.
Answers are grounded in this paper and cite their sources.
May inform more efficient HFMD lab surveillance without extra tests; leaves open prospective evaluation and application to other multi-serotype pathogens.
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.
No takes yet. Share an insight, caveat, or question.
Cheng et al. (2022) studied Hand foot and mouth disease (HFMD). Disease Surveillance Informatics Optimization and Simulation (DIOS) framework vs. Existing surveillance network was evaluated on Mean absolute error in estimated serotype-specific incidence rates (14.1% reduction). Applying the DIOS framework to optimize hand foot and mouth disease surveillance networks reduced the mean absolute error in estimated serotype-specific incidence rates by 14.1%.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: