Decision making and intervention against infectious diseases require analysis of large volumes of data, including demographic data, contact networks, age- specific contact rates, mobility networks, and healthcare and control intervention data and models. In this paper, we present our Networks-Of-Traces for Epidemic Spread Simulations (N0TES2) model and system which aim at assisting experts and helping them explore existing simulation trace data sets. N0TES2 supports analysis and indexing of simulation data sets as well as parameter and feature analysis, including identification of unknown dependencies across the input parameters and output variables spanning the different layers of the observation and simulation data.

NOTES2: Networks-of-traces for epidemic spread simulations

Sapino M. L.;
2015-01-01

Abstract

Decision making and intervention against infectious diseases require analysis of large volumes of data, including demographic data, contact networks, age- specific contact rates, mobility networks, and healthcare and control intervention data and models. In this paper, we present our Networks-Of-Traces for Epidemic Spread Simulations (N0TES2) model and system which aim at assisting experts and helping them explore existing simulation trace data sets. N0TES2 supports analysis and indexing of simulation data sets as well as parameter and feature analysis, including identification of unknown dependencies across the input parameters and output variables spanning the different layers of the observation and simulation data.
2015
29th AAAI Conference on Artificial Intelligence, AAAI 2015
Hyatt Regency in Austin, usa
2015
AAAI Workshop - Technical Report
AI Access Foundation
WS-15-06
79
83
Liu S.; Garg Y.; Candan K.S.; Sapino M.L.; Chowell-Puente G.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1788088
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