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

Abstract

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.

Geographic Variation and Environmental Predictors of Nontuberculous Mycobacteria in Laboratory Surveillance, Virginia, USA, 2021–20231

“…Previous studies of NTM epidemiology, often relying on data from retrospective review of electronic medical record databases, suggest NTM are increasing in incidence; the most common pathogens of clinical respiratory disease belong to Mycobacterium avium complex (MAC) and Mycobacterium abscessus ( 8 – 10 ). To date, information for epidemiologic research from laboratory surveillance for NTM such as MAC and M. abscessus has not been accessed as frequently as for some other pathogens of public health concern ( 11 – 15 ). Despite this, population-based studies of NTM have found that 86% of patients meeting the American Thoracic Society/Infectious Diseases Society of America microbiologic definition of NTM lung disease also met full clinical criteria for that disease, suggesting microbiologic laboratory-based data could be used for public health surveillance ( 16 ).…”

“…To date, information for epidemiologic research from laboratory surveillance for NTM such as MAC and M. abscessus has not been accessed as frequently as for some other pathogens of public health concern (11)(12)(13)(14)(15). Despite this, population-based studies of NTM have found that 86% of patients meeting the American Thoracic Society/Infectious Diseases Society of America microbiologic definition of NTM lung disease also met full clinical criteria for that disease, suggesting microbiologic laboratory-based data could be used for public health surveillance (16).…”