Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering

Mathematical Modeling for the Transmission of Ebola Virus

Authors
Anqi Li
Corresponding Author
Anqi Li
Available Online February 2016.
DOI
10.2991/iccsae-15.2016.46How to use a DOI?
Keywords
SEIR model; Ebola transmission model; Grey Prediction mode
Abstract

The goal of this model is to optimize the eradication of Ebola, which has been increasingly severe since 2014, causing more than 8966 deaths. Without effective control, human society will face a serious threat. This paper primarily addresses the problem by predicting the trend of Ebola transmission using the modified SIR model—SEIR model. Based on the data from the WHO, the parameters of this model are obtained. From this, epidemic situation in the future can be clearly observed. Besides, relatively accurate data in the short term can be gotten through this Grey Prediction model, which remedies the shortage of SEIR model in the short-term prediction. Thus, Ebola epidemic situation is bound to be eased and possibly be eradicated within a certain period.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
Series
Advances in Computer Science Research
Publication Date
February 2016
ISBN
10.2991/iccsae-15.2016.46
ISSN
2352-538X
DOI
10.2991/iccsae-15.2016.46How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Anqi Li
PY  - 2016/02
DA  - 2016/02
TI  - Mathematical Modeling for the Transmission of Ebola Virus
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 239
EP  - 244
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.46
DO  - 10.2991/iccsae-15.2016.46
ID  - Li2016/02
ER  -