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Manheim, D.

Publications and source records attributed to Manheim, D..

2 recordsLinked to original sources

Self-Limiting Factors in Pandemics and Multi-Disease Syndemics

The potential for an infectious disease outbreak that is much worse than those which have been observed in human history, whether engineered or natural, has been the focus of significant concern in biosecurity. Fundamental dynamics of disease spread make such outbreaks much less likely than they first appear. Here we present a slightly modified formulation of the typical SEIR model that illustrates these dynamics more clearly, and shows the unlikely cases where concern may still be warranted. This is then applied to an extreme version of proposed pandemic risk, multi-disease syndemics, to show that (absent much clearer reasons for concern) the suggested dangers are overstated.\n\nThe models used in this paper are available here: https://github.com/davidmanheim/Infectious-Disease-Models

epidemiology

A Generative Bayesian Approach for Incorporating Biosurveillance Sources into Epidemiological Models

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.

epidemiology