Methods to optimize surveillance networks for infectious diseases caused by subtypes of pathogens

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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Photo by DeepMind on Unsplash

Photo by DeepMind on Unsplash

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.