Abstract
We describe a stochastic method using Dirichlet processes to derive mixture models that allow the numerical description of outbreaks of diseases with multiple sources. We show that existing disease models may be extended using this method and how this may be used in a practical context to support the simulated response to a mass casualty public health emergency.
Original language | English |
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Title of host publication | Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017 |
Editors | Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 481-484 |
Number of pages | 4 |
ISBN (Electronic) | 9788394625375 |
DOIs | |
Publication status | Published - 10 Nov 2017 |
Event | 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017 - Prague, Czech Republic Duration: 3 Sept 2017 → 6 Sept 2017 |
Publication series
Name | Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017 |
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Conference
Conference | 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017 |
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Country/Territory | Czech Republic |
City | Prague |
Period | 3/09/17 → 6/09/17 |
Bibliographical note
Funding Information:Institute of Information and Communication Technologies Bulgarian Academy of Sciences acad. Georgi Bonchev bl. 2 Bulgaria Email: [email protected] This work has been funded by: the EC Research Executive Agency 7th Framework Programme, (SEC-2013.4.1-4) under grant number FP7-SEC-2013-608078-IMproving Preparedness and Response of HEalth Services in major criseS (IMPRESS), the UK NIH Health Protection Research Unit in Emergency Preparedness and Response and by grant 02/20 awarded from the Bulgarian National Science Fund.
Publisher Copyright:
© 2017 PTI.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
Keywords
- Chinese restaurant process
- clustering
- epidemiology
- mixture models
- non-parametric fitting
- stochastic processes