Abstract
In our paper we use the, recently proposed, model for simulating the process of disease spreading in the environment defined by the Cellular Automaton. The main effort goes to the analysis of the influence of cell size on the epidemic curves and other characteristics related to the studied process. We take into account some real data concerning the occupation in the city of Łódź, which has about 700000 inhabitants. The results show that by marshaling the parameters of simulation we can obtain explicitly different results. This comment applies to a lot of features like: the shape of epidemic curve, the total number of diseased or the amount of ill in particular areas/cells.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guo, D., Li, K.C., Peters, T.R., Snively, B.M., Poehling, K.A., Zhou, X.: Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it. Sci. Rep. 5, 8980 (2015)
Aron, J.L., Schwartz, I.B.: Seasonality and period-doubling bifurcations in an epidemic model. J. Theor. Biol. 110, 665–679 (1984)
Yi, N., Zhang, Q., Mao, K., Yang, D., Li, Q.: Analysis and control of an seir epidemic system with nonlinear transmission rate. Math. Comput. Model. 50, 1498–1513 (2009)
Schimit, P., Pereira, F.: Disease spreading in complex networks: a numerical study with principal component analysis. Expert Syst. Appl. 97, 41–50 (2018)
Keeling, M., Eames, K.T.D.: Networks and epidemic models. J. Roy. Soc. Interface 2, 295–307 (2005)
Hoya White, S., Martin del Rey, A., Rodriguez Sanchez, G.: Modeling epidemics using cellular automata. Appl. Math. Comput. 186, 193–202 (2007)
Pfeifer, B., et al.: A cellular automaton framework for infectious disease spread simulation. Open Med. Inform. J. 2, 70–81 (2008)
Sharma, N., Gupta, A.K.: Impact of time delay on the dynamics of SEIR epidemic model using cellular automata. Phys. A: Stat. Mech. Appl. 471, 114–125 (2017)
Holko, A., Medrek, M., Pastuszak, Z., Phusavat, K.: Epidemiological modeling with a population density map-based cellular automata simulation system. Expert Syst. Appl. 48, 1–8 (2016)
Lloyd, A.L.: Realistic distributions of infectious periods in epidemic models: changing patterns of persistence and dynamics. Theor. Popul. Biol. 60, 59–71 (2001)
Cartwright, F.F., Biddiss, M.D.: Disease and History, 2nd edn. Sutton Publishing, Stroud (2000)
Johnson, N., Mueller, J.: Updating the accounts: global mortality of the 1918–1920 spanish influenza pandemic. Bull. Hist. Med. 76, 105–115 (2002)
Knobler, S., Mack, A., Mahmoud, A. (eds.): The Threat of Pandemic Influenza: Are We Ready? Workshop Summary. Institute of Medicine (US) Forum on Microbial Threats, National Academies Press (US) (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Orzechowska, J., Fordon, D., Gwizdałła, T.M. (2018). Size Effect in Cellular Automata Based Disease Spreading Model. In: Mauri, G., El Yacoubi, S., Dennunzio, A., Nishinari, K., Manzoni, L. (eds) Cellular Automata. ACRI 2018. Lecture Notes in Computer Science(), vol 11115. Springer, Cham. https://doi.org/10.1007/978-3-319-99813-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-99813-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99812-1
Online ISBN: 978-3-319-99813-8
eBook Packages: Computer ScienceComputer Science (R0)