Volume 4, Issue 4, August 2019, Page: 74-78
An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach
Jerry John Kponyo, Department of Electrical Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Kenneth Coker, Department of Electrical Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Justice Owusu Agyemang, Department of Electrical Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Joyce Der, School of Public Health, University of Health and Allied Sciences, Ho, Ghana; London School of Hygiene and Tropical Medicine, London, United Kingdom
Received: Jan. 11, 2019;       Accepted: Aug. 7, 2019;       Published: Aug. 23, 2019
DOI: 10.11648/j.eas.20190404.11      View  115      Downloads  37
Abstract
The outbreaks of infectious diseases have had a huge impact on the human society. Researchers have developed models aimed at understanding how various infectious diseases spread in communities and also proposed control measures that can minimize or stop the spread of the diseases. Most researchers have developed stochastic mathematical models which are used in predicting the occurrence of an epidemic. Most of the proposed models do not employ the use of system dynamics hence making it difficult to adopt the same model in predicting the behavior of other epidemic diseases. This research work focuses on the use of system dynamics in predicting the extent of an epidemic spread so that effective preventive and quarantine measures can be put in place to curb that epidemic. The SIR model forms the basis of the model. The model was developed in NetLogo. Disease parameters and environmental conditions play a role in the spread of an epidemic. Due to this the parameters used in the model included initial population, infectiousness, fatality rate, days to recover, hygiene, vaccination, travel-openings and the number of doctors within the community. The efficiency of the developed model was tested using data from two disease outbreaks: Ebola and Influenza. The model proved itself to be efficient in predicting the infected and death cases which were very close to the real-life data.
Keywords
NetLogo, SIR, Epidemic, Influenza, Ebola
To cite this article
Jerry John Kponyo, Kenneth Coker, Justice Owusu Agyemang, Joyce Der, An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach, Engineering and Applied Sciences. Vol. 4, No. 4, 2019, pp. 74-78. doi: 10.11648/j.eas.20190404.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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