Individual-based lattice model for spatial spread of epidemics
arXiv:nlin/0207048 · doi:10.1155/S1026022601000206
Abstract
We present a lattice gas cellular automaton (LGCA) to study spatial and temporal dynamics of an epidemic of SIR (susceptible-infected-removed) type. The automaton is fully discrete, i.e. space, time and number of individuals are discrete variables. The automaton can be applied to study spread of epidemics in both human and animal populations. We investigate effects of spatial inhomogeneities in initial distribution of infected and vaccinated populations on the dynamics of epidemic of SIR type. We discuss vaccination strategies which differ only in spatial distribution of vaccinated individuals. Also, we derive an approximate, mean-field type description of the automaton, and discuss differences between the mean-field dynamics and the results of LGCA simulation.
13 pages, 5 figures
References in corpus (1)
Cited by in corpus (8)
- Evolutionary games on graphs
- Formation of spatial patterns in epidemic model with constant removal rate of the infectives
- Formation of regular spatial patterns in ratio-dependent predator-prey model driven by spatial colored-noise
- Effects of population mixing on the spread of SIR epidemics
- A Cellular Automata Model with Probability Infection and Spatial Dispersion
- Stationary states and spatial patterning in an epidemiology model with implicit mobility
- Spatial correlations in SIR epidemic models
- A Data-driven Understanding of COVID-19 Dynamics Using Sequential Genetic Algorithm Based Probabilistic Cellular Automata