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Mathematics of Planet EarthData-Informed Modeling in the Health Sciences

Mathematics of Planet Earth: Data-Informed Modeling in the Health Sciences [The adoption of automation and technology by health professionals is triggering an explosion of databases and data streams in that sector. The emergence of this data torrent creates the pressing need to mine it for value, which in turn requires investment for the development of modeling and analysis tools. In view of this, dynamicists are presented with the terrific opportunity to enrich their discipline by supplying it with new tools, expanding its scope, and elevating its social impact. This chapter is written in that spirit, examining three concrete case studies encountered in the field: quantifying the salmonellosis risk posed by distinct food sources, assimilating genetic data into a dynamical model for avian influenza transmission, and statistically decontaminating gas chromatography/mass spectroscopy time series. We review available prototypical models and build on them guided by data and mathematical abstraction, demonstrating in the process how to root a model into data. This takes us quite naturally into the realm of probabilistic and statistical modeling and reopens a decades-old discussion on the role of discrete models in applied mathematics. We also touch briefly on the timely subject of mathematicians being employed as such outside math departments and attempt a short outlook on their prospects and opportunities.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Mathematics of Planet EarthData-Informed Modeling in the Health Sciences

Part of the Mathematics of Planet Earth Book Series (volume 5)
Editors: Kaper, Hans G.; Roberts, Fred S.
Zagaris, Antonios
Mathematics of Planet Earth — Nov 2, 2019
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References (29)

Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2019
ISBN
978-3-030-22043-3
Pages
129 –173
DOI
10.1007/978-3-030-22044-0_6
Publisher site
See Chapter on Publisher Site

Abstract

[The adoption of automation and technology by health professionals is triggering an explosion of databases and data streams in that sector. The emergence of this data torrent creates the pressing need to mine it for value, which in turn requires investment for the development of modeling and analysis tools. In view of this, dynamicists are presented with the terrific opportunity to enrich their discipline by supplying it with new tools, expanding its scope, and elevating its social impact. This chapter is written in that spirit, examining three concrete case studies encountered in the field: quantifying the salmonellosis risk posed by distinct food sources, assimilating genetic data into a dynamical model for avian influenza transmission, and statistically decontaminating gas chromatography/mass spectroscopy time series. We review available prototypical models and build on them guided by data and mathematical abstraction, demonstrating in the process how to root a model into data. This takes us quite naturally into the realm of probabilistic and statistical modeling and reopens a decades-old discussion on the role of discrete models in applied mathematics. We also touch briefly on the timely subject of mathematicians being employed as such outside math departments and attempt a short outlook on their prospects and opportunities.]

Published: Nov 2, 2019

Keywords: Probabilistic and data-driven modeling; Parameter inference; Extramural mathematics; Infection source attribution; Mathematical epidemiology; Data decontamination; Bayesian hierarchical models

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