NDLI: Towards forecasting flu dynamics using a regionalized state space model

Content Provider	IEEE Xplore Digital Library
Author	Loganathan, P. Ho Chee Siang Hao Ran Lee Lakshminarayanan, S.
Copyright Year	2011
Description	Author affiliation: Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore (Loganathan, P.; Ho Chee Siang; Hao Ran Lee; Lakshminarayanan, S.)
Abstract	The emergence of H1N1 in 2009 and a subsequent pandemic onset illustrated the importance of developing effective models with useful predictive capabilities for infectious diseases. The early identification of epidemic peaks can help the authorities to strategize effective anti-epidemic plans. In this regard, we propose a particle filter approach using the Susceptible-Exposed-Infected and Recovered (SEIR) epidemic model. The epidemic model was integrated with an observation model characterizing real-time influenza-like illnesses (ILI) data originating from general practice / family doctors (GPFDs) located in Singapore. The systematic resampling algorithm used in our approach resulted in better parameter estimates and the approach was able to predict the ILI peak registered around Day 40. However, the current approach suffers a serious limitation in its sensitivity towards the initial prior distribution assumed. The shortcomings of the current approach can be overcome by using a regionalized approach. Future efforts will be dedicated towards initializing the prior based on region-specific socio-demographic variables. Such regionalized models can provide good insight concerning the execution of efficient region-specific anti-epidemic plans for preventing future pandemics.
Starting Page	175
Ending Page	180
File Size	923672
Page Count	6
File Format	PDF
ISBN	9781424474608
e-ISBN	9789881725509
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Zhejiang University
Subject Keyword	Training Smoothing methods Prediction algorithms Particle filters Mathematical model Kernel Diseases
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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