An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach

Jerry John Kponyo; Kenneth Coker; Justice Owusu Agyemang; Joyce Der

doi:doi:10.11648/j.eas.20190404.11

| Peer-Reviewed

An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach

Jerry John Kponyo, Kenneth Coker, Justice Owusu Agyemang, Joyce Der

Published in Engineering and Applied Sciences (Volume 4, Issue 4)

Received: 11 January 2019 Accepted: 7 August 2019 Published: 23 August 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	Engineering and Applied Sciences (Volume 4, Issue 4)
DOI	10.11648/j.eas.20190404.11
Page(s)	74-78
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

NetLogo, SIR, Epidemic, Influenza, Ebola

References

[1]	Tom Britton, Etienne Pardoux, “Stochastic epidemics on homogenous community”, arXiv: 1808.05350v1 [math.PR], pg. 5, August 2018.
[2]	Z. Bai and Y. Zhou, “Existence of two periodic solutions for a non-autonomous SIR epidemic model”, Appl. Math. Model. 35 (2011), pp. 382–391.
[3]	D. J. Earn, P. Rohani, B. M. Bolker, and B. T. Grenfell, A simple model for complex dynamical transitions in epidemics, Science 287 (2000), pp. 667–670.
[4]	D. Greenhalgh and I. A. Moneim, SIRS epidemic model and simulations using different types of seasonal contact rate, Syst. Anal. Model. Simul. 43 (2003), pp. 573–600.
[5]	J. Ma and Z. Ma, Epidemic threshold conditions for seasonally forced SEIR models, Math. Biosci. Eng. 3 (2006), pp. 161–172.
[6]	C. Milling, C. Caramanis, S. Mannor, “Detecting epidemics using highly noisy data: Identifying the causative network of an epidemic”, 2012.
[7]	M. Hall, R. Gani, H. E. Hughes, S. Leach, “Real-time epidemic forecasting for pandemic influenza”, Epidemol. Infect. (2007), 135, 372-385.
[8]	Serfling RE, “Methods for current statistical analysis of excess pnuemonia-influenza deaths”. Public Health Reports 1963; 78: 494–506.
[9]	Costagliola D, et al. “A routine tool for detection and assessment of epidemics of influenza-like-illness”, American Journal of Public Health 1991; 81: 97–99.
[10]	Toubiana L, Flahault A. A space-time criterion for early detection of epidemics of influenza-like-illness. European Journal of Epidemiology 1998; 14: 465–470.
[11]	Carrat F, Valleron AJ, “Epidemiologic mapping using the kriging method: application to an influenza-like illness epidemic in France”. American Journal of Epidemiology 1992; 135: 1293–1300.
[12]	Viboud C, et al. “Prediction of the spread of influenza epidemics by the method of analogues”. American Journal of Epidemiology 2003; 158: 996–1006.
[13]	Quenel P, Dab W. “Influenza A and B epidemic criteria based on time-series analysis of health services surveillance data”. European Journal of Epidemiology 1998; 14: 275–285.
[14]	Hashimoto S, et al. “Detection of epidemics in their early stage through infectious disease surveillance”. International Journal of Epidemiology 2000; 29: 905-910.
[15]	Mooney J, Wright E, Christie P. “Predictive modelling of influenza outbreaks: a linear regression analysis”. SCIEH Weekly Report 2001; 35: 134–135.
[16]	Mills CE, Robins JM, Lipsitch M. Transmissibility of 1918 pandemic influenza. Nature 2004; 432: 904-906.
[17]	Wallinga J, Teunis P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology 2004; 160: 509–516.

Cite This Article

Plain Text BibTeX RIS

APA Style

Jerry John Kponyo, Kenneth Coker, Justice Owusu Agyemang, Joyce Der. (2019). An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach. Engineering and Applied Sciences, 4(4), 74-78. https://doi.org/10.11648/j.eas.20190404.11

Copy | Download

ACS Style

Jerry John Kponyo; Kenneth Coker; Justice Owusu Agyemang; Joyce Der. An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach. Eng. Appl. Sci. 2019, 4(4), 74-78. doi: 10.11648/j.eas.20190404.11

Copy | Download

AMA Style

Jerry John Kponyo, Kenneth Coker, Justice Owusu Agyemang, Joyce Der. An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach. Eng Appl Sci. 2019;4(4):74-78. doi: 10.11648/j.eas.20190404.11

Copy | Download

@article{10.11648/j.eas.20190404.11,
  author = {Jerry John Kponyo and Kenneth Coker and Justice Owusu Agyemang and Joyce Der},
  title = {An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach},
  journal = {Engineering and Applied Sciences},
  volume = {4},
  number = {4},
  pages = {74-78},
  doi = {10.11648/j.eas.20190404.11},
  url = {https://doi.org/10.11648/j.eas.20190404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20190404.11},
  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.},
 year = {2019}
}

Copy | Download

TY - JOUR
T1 - An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach
AU - Jerry John Kponyo
AU - Kenneth Coker
AU - Justice Owusu Agyemang
AU - Joyce Der
Y1 - 2019/08/23
PY - 2019
N1 - https://doi.org/10.11648/j.eas.20190404.11
DO - 10.11648/j.eas.20190404.11
T2 - Engineering and Applied Sciences
JF - Engineering and Applied Sciences
JO - Engineering and Applied Sciences
SP - 74
EP - 78
PB - Science Publishing Group
SN - 2575-1468
UR - https://doi.org/10.11648/j.eas.20190404.11
AB - 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.
VL - 4
IS - 4
ER -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Jerry John Kponyo, Kenneth Coker, Justice Owusu Agyemang, Joyce Der. (2019). An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach. Engineering and Applied Sciences, 4(4), 74-78. https://doi.org/10.11648/j.eas.20190404.11

Copy | Download

ACS Style

Jerry John Kponyo; Kenneth Coker; Justice Owusu Agyemang; Joyce Der. An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach. Eng. Appl. Sci. 2019, 4(4), 74-78. doi: 10.11648/j.eas.20190404.11

Copy | Download

AMA Style

Jerry John Kponyo, Kenneth Coker, Justice Owusu Agyemang, Joyce Der. An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach. Eng Appl Sci. 2019;4(4):74-78. doi: 10.11648/j.eas.20190404.11

Copy | Download

@article{10.11648/j.eas.20190404.11,
  author = {Jerry John Kponyo and Kenneth Coker and Justice Owusu Agyemang and Joyce Der},
  title = {An Algorithm to Determine the Extent of an Epidemic Spread: A NetLogo Modeling Approach},
  journal = {Engineering and Applied Sciences},
  volume = {4},
  number = {4},
  pages = {74-78},
  doi = {10.11648/j.eas.20190404.11},
  url = {https://doi.org/10.11648/j.eas.20190404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20190404.11},
  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.},
 year = {2019}
}

Copy | Download