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bioRxiv · 10.1101/328518

A Generative Bayesian Approach for Incorporating Biosurveillance Sources into Epidemiological Models

Manheim, D.
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Abstract

Biosurveillance \"systematically collects and analyzes data for the purpose of detecting cases of disease, [and] outbreaks of disease.\" (Wagner, Moore and Aryel, 2006) This typically involves using a set of known sources of epidemiological data, instead of opportunistically using the data sources which become available over time. This work attempts to partially remedy that limitation by using an easily adapted generative Bayesian econometric model to allow incorporation of novel data sources. This is done by building a generative model of the information sources, then using Bayesian Markov-chain Monte-Carlo to find the relationships between data and actual caseloads to use in an epidemiological model 1. While the application presented is limited to three data sources for a single disease (influenza), the methodology is potentially widely applicable, and enables rapid incorporation of a variety of sources and source types.

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Manheim, D.. 2018-05-22. A Generative Bayesian Approach for Incorporating Biosurveillance Sources into Epidemiological Models. https://doi.org/10.1101/328518

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