z-logo
open-access-imgOpen Access
Epidemic Spread Modeling with Time Variant Infective Population Using Pushdown Cellular Automata
Author(s) -
Senthil Athithan,
V. P. Shukla,
S. R. Biradar
Publication year - 2014
Publication title -
journal of computational environmental sciences
Language(s) - English
Resource type - Journals
eISSN - 2356-7279
pISSN - 2314-8292
DOI - 10.1155/2014/769064
Subject(s) - cellular automaton , population , computer science , epidemic model , pandemic , covid-19 , disease , artificial intelligence , infectious disease (medical specialty) , medicine , environmental health , pathology
The world without a disease is a dream of any human being. The disease spread if not controlled could cause an epidemic situation to spread and lead to pandemic. To control an epidemic we need to understand the nature of its spread and the epidemic spread model helps us in achieving this. Here we propose an epidemic spread model which considers not only the current infective population around the population but also the infective population which remain from the previous generations for computing the next generation infected individuals. A pushdown cellular automata model which is an enhanced version of cellular automata by adding a stack component is being used to model the epidemic spread and the model is validated by the real time data of H1N1 epidemic in Abu Dhabi.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom