In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 9 ( 2022-9-27), p. e1010575-
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010575
DOI:
10.1371/journal.pcbi.1010575.g001
DOI:
10.1371/journal.pcbi.1010575.g002
DOI:
10.1371/journal.pcbi.1010575.g003
DOI:
10.1371/journal.pcbi.1010575.g004
DOI:
10.1371/journal.pcbi.1010575.g005
DOI:
10.1371/journal.pcbi.1010575.g006
DOI:
10.1371/journal.pcbi.1010575.g007
DOI:
10.1371/journal.pcbi.1010575.s001
DOI:
10.1371/journal.pcbi.1010575.s002
DOI:
10.1371/journal.pcbi.1010575.s003
DOI:
10.1371/journal.pcbi.1010575.s004
DOI:
10.1371/journal.pcbi.1010575.s005
DOI:
10.1371/journal.pcbi.1010575.s006
DOI:
10.1371/journal.pcbi.1010575.s007
DOI:
10.1371/journal.pcbi.1010575.s008
DOI:
10.1371/journal.pcbi.1010575.s009
DOI:
10.1371/journal.pcbi.1010575.s010
DOI:
10.1371/journal.pcbi.1010575.s011
DOI:
10.1371/journal.pcbi.1010575.s012
DOI:
10.1371/journal.pcbi.1010575.s013
DOI:
10.1371/journal.pcbi.1010575.r001
DOI:
10.1371/journal.pcbi.1010575.r002
DOI:
10.1371/journal.pcbi.1010575.r003
DOI:
10.1371/journal.pcbi.1010575.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
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