What is it about?
Laboratory-based tests can determine the specific agents that cause infectious diseases, providing important information for disease surveillance. However, pathogen typing is relatively expensive and scarce. We develop a simulation framework to solve the problem of optimal allocation of laboratory-typing within clinical surveillance systems, optimizing allocation of laboratory-typing across locations and clinical subgroups (e.g., severe vs. mild cases).
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Why is it important?
Using a series of simulation-optimization studies, we identified surveillance network designs that are capable of reducing the mean absolute error of serotype-specific incidence rates, and demonstrate how this powerful framework can be used to better leverage laboratory-typing infrastructure to track pathogen-specific epidemiologic trends.
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This page is a summary of: Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections, PLoS Computational Biology, September 2022, PLOS, DOI: 10.1371/journal.pcbi.1010575.
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