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.

Shared Differential Expression-Based Distance Reflects Global Cell Type Relationships in Single-Cell RNA Sequencing Data.

Unsupervised cell clustering on the basis of meaningful biological variation in single-cell RNA sequencing (scRNA seq) data has received significant attention, as it assists with ontological subpopulation identification among the data. A key step in the clustering process is to compute distances between the cells under a specified distance measure. Although particular distance measures may successfully separate cells into biologically relevant clusters, they may fail to retain global structure of the data, such as relative similarity between the cell clusters. In this article, we modify a biologically motivated distance measure, SIDEseq, for use of aggregate comparisons of cell types in large single-cell assays, and demonstrate that, across simulated and real scRNA seq data, the distance matrix more consistently retains global cell type relationships than commonly used distance measures for scRNA seq clustering. We call the modified distance measure "SIDEREF." We explore spectral dimension reduction of the SIDEREF distance matrix as a means of noise filtering, similar to principal components analysis applied directly to expression data. We utilize a summary measure of relative cell type distances to better display the cell group relationships. SIDEREF visualizations more consistently reflect global structures in the data than other commonly considered distance measures. We utilize relative cell type distances and the SIDEREF distance measure to uncover compositional differences between annotated leukocyte cell groups in a compendium of Mus musculus scRNA seq assays comprising 12 tissues. SIDEREF and associated analysis is openly available on GitHub.