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Using Data Assimilation Technique and Epidemic Model to Predict TB Epidemic

by Himanshu Gupta, Kamal Kant Verma, Punit Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 9
Year of Publication: 2015
Authors: Himanshu Gupta, Kamal Kant Verma, Punit Sharma
10.5120/ijca2015906625

Himanshu Gupta, Kamal Kant Verma, Punit Sharma . Using Data Assimilation Technique and Epidemic Model to Predict TB Epidemic. International Journal of Computer Applications. 128, 9 ( October 2015), 1-5. DOI=10.5120/ijca2015906625

@article{ 10.5120/ijca2015906625,
author = { Himanshu Gupta, Kamal Kant Verma, Punit Sharma },
title = { Using Data Assimilation Technique and Epidemic Model to Predict TB Epidemic },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 9 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number9/22898-2015906625/ },
doi = { 10.5120/ijca2015906625 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:21:09.346749+05:30
%A Himanshu Gupta
%A Kamal Kant Verma
%A Punit Sharma
%T Using Data Assimilation Technique and Epidemic Model to Predict TB Epidemic
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 9
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

People of India are very susceptible to many infectious diseases like malaria, TB, HIV etc. There are many epidemic models that are used to predict new cases of disease. Some of the popular epidemic models are SI (Susceptible-Infectious), SIR (Susceptible-Infectious-Recovered), SIRS, SIS etc. In this research quarterly data of TB disease in Uttarakhand (India) for 7 years is collected and on the basis of this data new infected population in the next quarter is predicted using SIR epidemic model and data assimilation technique (Ensemble Kalman Filter). Analysis and implementation is done in MATLAB. Results show good agreement to measured values.

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Index Terms

Computer Science
Information Sciences

Keywords

Epidemics Infectious Disease Disease Dynamics spatial-temporal SIR model & equations Data Assimilation Ensemble Kalman Filter Matlab Kalman gain Matrix

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